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Object detection + tracking + counting using Ultralytics YOLO26 🚀 👉 Object detection tells you what is in a scene. 👉 Tracking follows each object across video frames. 👉 Counting turns those movements into actionable data. YOLO26 delivers fast, accurate detections, while multi-object tracking maintains unique identities across frames....

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Muhammad Rizwan Munawar

2,060 subscribers

16,069 Aufrufe • vor 15 Tagen •via X (Twitter)

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Real-time bakery item counting using Ultralytics YOLO26 😍 In food production lines, counting sounds simple until it isn’t. Items move fast, overlap on conveyors, change orientation, and sometimes partially occlude each other. Whether it’s ice-cream cones on a belt or cream being filled into nests, maintaining accurate counts in real time is critical for quality control and throughput. More info 👇 #Bakery #Retail #MachineLearning
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Real-time bakery item counting using Ultralytics YOLO26 😍 In food production lines, counting sounds simple until it isn’t. Items move fast, overlap on conveyors, change orientation, and sometimes partially occlude each other. Whether it’s ice-cream cones on a belt or cream being filled into nests, maintaining accurate counts in real time is critical for quality control and throughput. More info 👇 #Bakery #Retail #MachineLearning

Muhammad Rizwan Munawar

25,151 Aufrufe • vor 2 Monaten

Robots can now reconstruct 3D scenes in real time from a single RGB camera. [📍 Projects page + paper] No depth sensor. No retraining. 30 FPS. Researchers at the Imperial College London introduced KV-Tracker, a training-free method that makes heavy models like π³ and Depth Anything 3 fast enough for real-time tracking. The idea is simple. These models use global self-attention, which is powerful but computationally expensive. KV-Tracker caches the key and value pairs from selected keyframes and reuses them for new frames. That cache becomes an implicit scene representation. Result: • Up to 30 FPS • 10 to 15x speedup • Accurate 6-DoF tracking on benchmarks like TUM RGB-D and 7-Scenes • Works with monocular RGB only It also supports object-level tracking with masks and allows saving the KV-cache for later reuse. For robotics, this reduces hardware constraints and moves real-time 3D perception closer to practical deployment. Credit to Marwan Taher (Marwan Taher) at Imperial’s Dyson Robotics Lab and many others who contributed to this! 📍 Save projects page + paper for later: Video: ——- if it matters in AI or Robotics you'll read it here first:
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Robots can now reconstruct 3D scenes in real time from a single RGB camera. [📍 Projects page + paper] No depth sensor. No retraining. 30 FPS. Researchers at the Imperial College London introduced KV-Tracker, a training-free method that makes heavy models like π³ and Depth Anything 3 fast enough for real-time tracking. The idea is simple. These models use global self-attention, which is powerful but computationally expensive. KV-Tracker caches the key and value pairs from selected keyframes and reuses them for new frames. That cache becomes an implicit scene representation. Result: • Up to 30 FPS • 10 to 15x speedup • Accurate 6-DoF tracking on benchmarks like TUM RGB-D and 7-Scenes • Works with monocular RGB only It also supports object-level tracking with masks and allows saving the KV-cache for later reuse. For robotics, this reduces hardware constraints and moves real-time 3D perception closer to practical deployment. Credit to Marwan Taher (Marwan Taher) at Imperial’s Dyson Robotics Lab and many others who contributed to this! 📍 Save projects page + paper for later: Video: ——- if it matters in AI or Robotics you'll read it here first:

Ilir Aliu

53,911 Aufrufe • vor 4 Monaten

Household chores created using a movement sheet as reference to animate the entire scene using ChatGPT Image 2.0 and Seedance 2.0 on Yapper GPT Image 2 Prompt; [VISUAL STYLE] Monochrome grayscale composition featuring a highly detailed 3D-rendered female character. Designed like a professional instructional guide with a technical, diagram-inspired layout. Clean white background, soft studio lighting, and strong contrast to highlight posture, actions, and object interaction. [GRID LAYOUT] Structured 4×4 panel grid (16 frames total), evenly spaced with thin black divider lines. Each panel is identical in size and clearly numbered from 1 to 16, showing a continuous sequence of household activities. [CHARACTER] Use the provided reference image for the face and overall likeness. Same facial features, skin tone, and proportions Natural makeup, soft expression Consistent identity across all 16 panels Realistic proportions and clean hairstyle (loose or tied back) [WARDROBE] Modern, modest casual outfit: Fitted crop top (not revealing, clean neckline, practical for movement) High-waisted straight or slightly wide-leg jeans (full length, neat fit) Optional minimal sneakers or barefoot indoor styling Fabric should react naturally to movement (subtle folds and tension) [SCENE APPROACH] Minimal, clean environment per panel — only essential props related to the chore. No clutter, no complex backgrounds — focus stays on the subject and action. [PANEL STRUCTURE – EACH FRAME] Top-left: Step number + task title (e.g., “Step 4 – Vacuum Floor”) Center: Full-body pose performing the chore Bottom-left: 3–4 concise instruction lines Overlay: Motion arrows and directional guides showing action flow [CHORE SEQUENCE EXAMPLES] Make the Bed Tidy Up Room Dust Surfaces Vacuum Floor Sweep Floor Mop Floor Do Laundry Hang Clothes Fold Clothes Clean Kitchen Counter Wash Dishes Take Out Trash Water Plants Clean Bathroom Organize Shelves Final Room Reset [MOTION INDICATORS] Curved arrows → fluid actions (wiping, folding) Straight arrows → directional movement Circular arrows → repetitive motions (scrubbing, mopping) [RENDER QUALITY] High-detail sculpted 3D style with smooth grayscale shading, soft shadows, and clean linework. Polished, concept-art level finish with clarity in every pose and object interaction. [RESTRICTIONS] No color, no unnecessary background detail, no extra characters, no revealing clothing, no clutter — only the subject, props, and instructional elements.
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Household chores created using a movement sheet as reference to animate the entire scene using ChatGPT Image 2.0 and Seedance 2.0 on Yapper GPT Image 2 Prompt; [VISUAL STYLE] Monochrome grayscale composition featuring a highly detailed 3D-rendered female character. Designed like a professional instructional guide with a technical, diagram-inspired layout. Clean white background, soft studio lighting, and strong contrast to highlight posture, actions, and object interaction. [GRID LAYOUT] Structured 4×4 panel grid (16 frames total), evenly spaced with thin black divider lines. Each panel is identical in size and clearly numbered from 1 to 16, showing a continuous sequence of household activities. [CHARACTER] Use the provided reference image for the face and overall likeness. Same facial features, skin tone, and proportions Natural makeup, soft expression Consistent identity across all 16 panels Realistic proportions and clean hairstyle (loose or tied back) [WARDROBE] Modern, modest casual outfit: Fitted crop top (not revealing, clean neckline, practical for movement) High-waisted straight or slightly wide-leg jeans (full length, neat fit) Optional minimal sneakers or barefoot indoor styling Fabric should react naturally to movement (subtle folds and tension) [SCENE APPROACH] Minimal, clean environment per panel — only essential props related to the chore. No clutter, no complex backgrounds — focus stays on the subject and action. [PANEL STRUCTURE – EACH FRAME] Top-left: Step number + task title (e.g., “Step 4 – Vacuum Floor”) Center: Full-body pose performing the chore Bottom-left: 3–4 concise instruction lines Overlay: Motion arrows and directional guides showing action flow [CHORE SEQUENCE EXAMPLES] Make the Bed Tidy Up Room Dust Surfaces Vacuum Floor Sweep Floor Mop Floor Do Laundry Hang Clothes Fold Clothes Clean Kitchen Counter Wash Dishes Take Out Trash Water Plants Clean Bathroom Organize Shelves Final Room Reset [MOTION INDICATORS] Curved arrows → fluid actions (wiping, folding) Straight arrows → directional movement Circular arrows → repetitive motions (scrubbing, mopping) [RENDER QUALITY] High-detail sculpted 3D style with smooth grayscale shading, soft shadows, and clean linework. Polished, concept-art level finish with clarity in every pose and object interaction. [RESTRICTIONS] No color, no unnecessary background detail, no extra characters, no revealing clothing, no clutter — only the subject, props, and instructional elements.

Johnn

72,695 Aufrufe • vor 3 Monaten

📢 Our lab has been exploring 3D world models for years — and we’re thrilled to share **PhysTwin**: a milestone that reconstructs object appearance, geometry, and dynamics from just a few seconds of interaction! Led by the amazing Hanxiao Jiang 👉 PhysTwin combines **Gaussian splatting** with **inverse dynamics optimization** based on simple **spring-mass** systems. ⚙️ The result? Real-time, action-conditioned 3D video prediction under novel interactions (i.e., 3D world models). 🔑 A few key takeaways: 1. Having the right structure (e.g., particles/masses) helps navigate the trade-off between sample efficiency, generalization, and broad applicability. 2. Visual foundation models (VFMs) have matured to the point where they can provide rich supervision for world modeling (e.g., tracking, shape completion). 3. Beyond VFMs, many crucial components have come together in recent years: Gaussian splats for rendering, NVIDIA Warp for high-performance simulation, and scene/asset generation from a wide range of labs and companies. The future of 3D world models is looking bright! ✨ 4. The resulting digital twin supports a wide range of downstream applications—especially in data generation and policy evaluation, thanks to its realistic rendering and simulation capabilities. 🎥 All code and data to reproduce the results, along with interactive demos, are available on the website. Check the following visualizations of: (1) observations, (2) reconstructed state/actions, (3) interactive digital twins, and (4) the overlays between real-world robot teleoperation and our model’s open-loop predictions.
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📢 Our lab has been exploring 3D world models for years — and we’re thrilled to share **PhysTwin**: a milestone that reconstructs object appearance, geometry, and dynamics from just a few seconds of interaction! Led by the amazing Hanxiao Jiang 👉 PhysTwin combines **Gaussian splatting** with **inverse dynamics optimization** based on simple **spring-mass** systems. ⚙️ The result? Real-time, action-conditioned 3D video prediction under novel interactions (i.e., 3D world models). 🔑 A few key takeaways: 1. Having the right structure (e.g., particles/masses) helps navigate the trade-off between sample efficiency, generalization, and broad applicability. 2. Visual foundation models (VFMs) have matured to the point where they can provide rich supervision for world modeling (e.g., tracking, shape completion). 3. Beyond VFMs, many crucial components have come together in recent years: Gaussian splats for rendering, NVIDIA Warp for high-performance simulation, and scene/asset generation from a wide range of labs and companies. The future of 3D world models is looking bright! ✨ 4. The resulting digital twin supports a wide range of downstream applications—especially in data generation and policy evaluation, thanks to its realistic rendering and simulation capabilities. 🎥 All code and data to reproduce the results, along with interactive demos, are available on the website. Check the following visualizations of: (1) observations, (2) reconstructed state/actions, (3) interactive digital twins, and (4) the overlays between real-world robot teleoperation and our model’s open-loop predictions.

Yunzhu Li

25,279 Aufrufe • vor 1 Jahr

You can't 3D reconstruct glass from images... ...WRONG! Thanks for video diffusion, now just about anything is possible! Introducing...Diffusion Knows Transparency (DKT) Transparent and reflective objects usually break robot vision and photogrammetry pipelines because they don't follow the "solid object" rules standard cameras expect. DKT is a new AI model that repurposes the "internal physics engine" found in video generation models to solve this problem. Researchers took a massive video diffusion model (WAN) and fine-tuned it using a custom-built synthetic dataset to turn it into a high-precision depth sensor. To train the AI, they built the first massive synthetic video library of transparent objects, 1.32 million frames of perfectly labeled glass and metal objects in motion. Without ever seeing a "real" labeled video of glass during training, the model (DKT) outperformed all previous specialized systems on real-world benchmarks (ClearPose, DREDS). They created a "lightweight" 1.3B parameter version that runs fast enough (0.17s per frame) to be used on actual robot hardware. Two reasons I find this project important: 1. It further proves that synthetic data will be essential for training the next generation vision models. 2. In real-world robotic tests, using DKT's depth maps nearly doubled the success rate of robot arms trying to pick up objects on tricky reflective or translucent surfaces. At home robots will need to interact with these types of objects on a daily basis. Check out the project page here: Code is LIVE! #Computervision #Robotics #AI
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You can't 3D reconstruct glass from images... ...WRONG! Thanks for video diffusion, now just about anything is possible! Introducing...Diffusion Knows Transparency (DKT) Transparent and reflective objects usually break robot vision and photogrammetry pipelines because they don't follow the "solid object" rules standard cameras expect. DKT is a new AI model that repurposes the "internal physics engine" found in video generation models to solve this problem. Researchers took a massive video diffusion model (WAN) and fine-tuned it using a custom-built synthetic dataset to turn it into a high-precision depth sensor. To train the AI, they built the first massive synthetic video library of transparent objects, 1.32 million frames of perfectly labeled glass and metal objects in motion. Without ever seeing a "real" labeled video of glass during training, the model (DKT) outperformed all previous specialized systems on real-world benchmarks (ClearPose, DREDS). They created a "lightweight" 1.3B parameter version that runs fast enough (0.17s per frame) to be used on actual robot hardware. Two reasons I find this project important: 1. It further proves that synthetic data will be essential for training the next generation vision models. 2. In real-world robotic tests, using DKT's depth maps nearly doubled the success rate of robot arms trying to pick up objects on tricky reflective or translucent surfaces. At home robots will need to interact with these types of objects on a daily basis. Check out the project page here: Code is LIVE! #Computervision #Robotics #AI

Jonathan Stephens

17,712 Aufrufe • vor 7 Monaten

OpenClaw setup made me $23,472 Literally overnight my $100 turned into $2,411 Average bot win rate 71% Copytrade: Here is the full strategy: The system builds automated workflows for trading by turning domain expertise into structured skills that activate automatically when specific market conditions appear Skill architecture Each skill is a modular package that includes instruction scripts and reference data This allows the system to apply specialized workflows without needing manual input for every trade Progressive context loading Skills use a three layer structure Only minimal metadata loads at first Full instructions historical data and supporting resources load only when required This reduces resource usage while keeping advanced trading capability Trigger detection Skills activate automatically when market conditions match predefined triggers such as volatility levels orderflow behavior or news sentiment This ensures the right workflow is used at the right time without manual action Workflow execution Once activated each skill runs a predefined multi step process including Real time price tracking and order book analysis Factor generation and backtesting Signal aggregation from machine learning models news sentiment and orderflow Risk assessment and capital allocation Trade execution with retries and position splitting Consistency and reliability All workflows are embedded directly into the system which ensures consistent execution instead of random decision making Every factor signal and risk rule is applied in a structured way Testing and iteration Skills are continuously improved using historical backtesting simulated trading and live performance tracking to maintain reliability in real market conditions Automation edge Instead of creating new strategies every time the system repeatedly uses optimized workflows This reduces complexity increases consistency and scales performance across thousands of trades Performance snapshot Started one month ago with $500 Current daily profit $2,300 per day Morning profit today $71,452 The system runs fully autonomously constantly scanning markets generating signals auditing trades managing risk and executing orders to maximize compounding returns
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OpenClaw setup made me $23,472 Literally overnight my $100 turned into $2,411 Average bot win rate 71% Copytrade: Here is the full strategy: The system builds automated workflows for trading by turning domain expertise into structured skills that activate automatically when specific market conditions appear Skill architecture Each skill is a modular package that includes instruction scripts and reference data This allows the system to apply specialized workflows without needing manual input for every trade Progressive context loading Skills use a three layer structure Only minimal metadata loads at first Full instructions historical data and supporting resources load only when required This reduces resource usage while keeping advanced trading capability Trigger detection Skills activate automatically when market conditions match predefined triggers such as volatility levels orderflow behavior or news sentiment This ensures the right workflow is used at the right time without manual action Workflow execution Once activated each skill runs a predefined multi step process including Real time price tracking and order book analysis Factor generation and backtesting Signal aggregation from machine learning models news sentiment and orderflow Risk assessment and capital allocation Trade execution with retries and position splitting Consistency and reliability All workflows are embedded directly into the system which ensures consistent execution instead of random decision making Every factor signal and risk rule is applied in a structured way Testing and iteration Skills are continuously improved using historical backtesting simulated trading and live performance tracking to maintain reliability in real market conditions Automation edge Instead of creating new strategies every time the system repeatedly uses optimized workflows This reduces complexity increases consistency and scales performance across thousands of trades Performance snapshot Started one month ago with $500 Current daily profit $2,300 per day Morning profit today $71,452 The system runs fully autonomously constantly scanning markets generating signals auditing trades managing risk and executing orders to maximize compounding returns

winkle.

44,158 Aufrufe • vor 4 Monaten

🚀 Introducing EgoExo Forge - built on top of Rerun, Gradio, and Hugging Face hub (I’ll be in San Francisco July 21–29 — if you’re into robotics, egocentric AI, large-scale data collection, or just want to chat, DM me!) In my opinion, large-scale, diverse, and high-quality data is still the largest bottleneck for generalized robotics deployment. I believe that some version of imitation learning from human examples will be the most scalable + clean way to train humanoid robots 🤖 (similar to what Tesla did for Full Self Driving). Teleop is too expensive to collect a large enough dataset in a reasonable manner, so passive collection via egocentric (and in certain cases, exocentric) views feels like the right bet. Over the past few months, I've been trying to build out the scaffolding for this and using Rerun as my underlying infrastructure. Data being collected needs to be easily inspectable + time series and rerun provides the right tooling for this. My goal is to first build out a ground truth representative dataset from already existing open source data, generate some reasonable baselines, and then go out and collect my own data that adheres to the defined schema. 🔍 Starting with open-source datasets 1. EgoDex from Apple 2. HOCap from Nvidia and the University of Texas at Dallas 3. Assembly101 from Meta All these different datasets have different sensor configurations + annotations, so my goal with egoexo-forge is to have one consistent labeling scheme + data layout. I built a data pipeline that aligns all of the different datasets in one general schema assuming the COCO133 keypoint layout that allows for exo+ego, ego only, or exo only Since the scaffolding is already there, it becomes MUCH easier to add other datasets. So the next ones that I'll be including are HD-EPIC kitchens dataset, HOT3D, and finally my own personal iPhone + insta360 go collection method. Once I have a diverse variety of datasets, I'll double down on what I believe to be the key algorithms required to make useful data for imitation learning 📊 1. Camera Pose estimation via SLAM/SFM for ego perspective (and automatic calibration for exo) 2. Human pose estimation for both egocentric + exocentric views 3. Metric 3D reconstruction + object tracking I'll be setting up reasonable open-source baselines for each of these to validate that these datasets work, and then finally try to use the generated datasets for some imitation learning via the pi0-lerobot repo I've been working on. I plan on making a blog post + providing more info on all of this in the near future so stay tuned
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🚀 Introducing EgoExo Forge - built on top of Rerun, Gradio, and Hugging Face hub (I’ll be in San Francisco July 21–29 — if you’re into robotics, egocentric AI, large-scale data collection, or just want to chat, DM me!) In my opinion, large-scale, diverse, and high-quality data is still the largest bottleneck for generalized robotics deployment. I believe that some version of imitation learning from human examples will be the most scalable + clean way to train humanoid robots 🤖 (similar to what Tesla did for Full Self Driving). Teleop is too expensive to collect a large enough dataset in a reasonable manner, so passive collection via egocentric (and in certain cases, exocentric) views feels like the right bet. Over the past few months, I've been trying to build out the scaffolding for this and using Rerun as my underlying infrastructure. Data being collected needs to be easily inspectable + time series and rerun provides the right tooling for this. My goal is to first build out a ground truth representative dataset from already existing open source data, generate some reasonable baselines, and then go out and collect my own data that adheres to the defined schema. 🔍 Starting with open-source datasets 1. EgoDex from Apple 2. HOCap from Nvidia and the University of Texas at Dallas 3. Assembly101 from Meta All these different datasets have different sensor configurations + annotations, so my goal with egoexo-forge is to have one consistent labeling scheme + data layout. I built a data pipeline that aligns all of the different datasets in one general schema assuming the COCO133 keypoint layout that allows for exo+ego, ego only, or exo only Since the scaffolding is already there, it becomes MUCH easier to add other datasets. So the next ones that I'll be including are HD-EPIC kitchens dataset, HOT3D, and finally my own personal iPhone + insta360 go collection method. Once I have a diverse variety of datasets, I'll double down on what I believe to be the key algorithms required to make useful data for imitation learning 📊 1. Camera Pose estimation via SLAM/SFM for ego perspective (and automatic calibration for exo) 2. Human pose estimation for both egocentric + exocentric views 3. Metric 3D reconstruction + object tracking I'll be setting up reasonable open-source baselines for each of these to validate that these datasets work, and then finally try to use the generated datasets for some imitation learning via the pi0-lerobot repo I've been working on. I plan on making a blog post + providing more info on all of this in the near future so stay tuned

Pablo Vela

32,085 Aufrufe • vor 1 Jahr

🚨 OFFICIAL ANNOUNCEMENT!🚨 Pakistan🇵🇰 has officially announced to inducted China’s HQ-19 Air Defense System 🇵🇰✅🇨🇳—a true game-changer in missile defense and space warfare! 🎯 Target: Indian Ballistic Missiles (Agni Series) India’s Agni series ballistic missiles, known for their long range and nuclear capability, are among the most serious strategic threats in the region. The HQ-19 system uses advanced phased-array radars and infrared sensors to detect and track these missiles immediately after launch, often while they’re still ascending. It calculates their trajectory in real-time, enabling HQ-19 to fire its interceptor missiles that use a “hit-to-kill” kinetic impactor — meaning the interceptor destroys the target by direct collision without explosives. This technology ensures high accuracy and reliability against fast-moving, high-altitude targets like the Agni missiles. 🛰️ Anti-Satellite (ASAT) Capability HQ-19’s capability isn’t limited to missiles — it can also target and destroy satellites in low Earth orbit (LEO), including Indian military reconnaissance and communication satellites. By using high-precision tracking radars and infrared sensors, HQ-19 locks onto satellites orbiting at altitudes of 200–2,000 km. It launches interceptor missiles equipped with kinetic kill vehicles (KKV) designed to collide with satellites at extremely high speeds, destroying them through sheer impact force. This effectively neutralizes enemy space-based surveillance, communication, and navigation assets — crippling India’s space advantage. ⚙️ How Does HQ-19 Intercept? 1. Detection: Powerful radars scan the skies to identify ballistic missile launches or satellite movements. 2. Tracking & Targeting: Infrared sensors track the heat signature, while onboard computers predict the exact trajectory. 3. Interceptor Launch: HQ-19 fires a fast, maneuverable interceptor missile equipped with a kinetic kill vehicle. 4. Kill Vehicle Guidance: The kill vehicle uses onboard sensors and thrusters to adjust its path mid-flight, ensuring a direct collision with the target. 5. Destruction: The interceptor collides with the missile or satellite at extremely high velocity, destroying it through kinetic energy alone—no explosives needed. ⚡ This official procurement dramatically boosts Pakistan’s defense against India’s missile threat and space assets — signaling a new era of air and space dominance! 👉 With HQ-19, Pakistan sends a crystal-clear message: No missile, no satellite is untouchable anymore! 💪🇵🇰
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🚨 OFFICIAL ANNOUNCEMENT!🚨 Pakistan🇵🇰 has officially announced to inducted China’s HQ-19 Air Defense System 🇵🇰✅🇨🇳—a true game-changer in missile defense and space warfare! 🎯 Target: Indian Ballistic Missiles (Agni Series) India’s Agni series ballistic missiles, known for their long range and nuclear capability, are among the most serious strategic threats in the region. The HQ-19 system uses advanced phased-array radars and infrared sensors to detect and track these missiles immediately after launch, often while they’re still ascending. It calculates their trajectory in real-time, enabling HQ-19 to fire its interceptor missiles that use a “hit-to-kill” kinetic impactor — meaning the interceptor destroys the target by direct collision without explosives. This technology ensures high accuracy and reliability against fast-moving, high-altitude targets like the Agni missiles. 🛰️ Anti-Satellite (ASAT) Capability HQ-19’s capability isn’t limited to missiles — it can also target and destroy satellites in low Earth orbit (LEO), including Indian military reconnaissance and communication satellites. By using high-precision tracking radars and infrared sensors, HQ-19 locks onto satellites orbiting at altitudes of 200–2,000 km. It launches interceptor missiles equipped with kinetic kill vehicles (KKV) designed to collide with satellites at extremely high speeds, destroying them through sheer impact force. This effectively neutralizes enemy space-based surveillance, communication, and navigation assets — crippling India’s space advantage. ⚙️ How Does HQ-19 Intercept? 1. Detection: Powerful radars scan the skies to identify ballistic missile launches or satellite movements. 2. Tracking & Targeting: Infrared sensors track the heat signature, while onboard computers predict the exact trajectory. 3. Interceptor Launch: HQ-19 fires a fast, maneuverable interceptor missile equipped with a kinetic kill vehicle. 4. Kill Vehicle Guidance: The kill vehicle uses onboard sensors and thrusters to adjust its path mid-flight, ensuring a direct collision with the target. 5. Destruction: The interceptor collides with the missile or satellite at extremely high velocity, destroying it through kinetic energy alone—no explosives needed. ⚡ This official procurement dramatically boosts Pakistan’s defense against India’s missile threat and space assets — signaling a new era of air and space dominance! 👉 With HQ-19, Pakistan sends a crystal-clear message: No missile, no satellite is untouchable anymore! 💪🇵🇰

Defense Intelligence

20,333 Aufrufe • vor 1 Jahr

We have released Seedance 2.0. Due to the 2500-character limit, please translate the following prompts into Chinese before use. [Technical Specs] Generate a 10-second, 16:9, 720p cinematic video. Smooth continuous camera motion with no cuts. The overall pacing is fast and tightly compressed, with rapid escalation from start to finish. Audio evolves quickly from a high-performance engine idle into intricate mechanical shifting and clicks, culminating in a soft electronic chime and the distinct sound of a "mwah" blowing kiss. [Global Constraints] Only the evolving mechanical character appears; no other humans or characters. All transformations must follow physical logic and maintain structural continuity. No object should pass through or intersect with other solid objects. Every robotic component must originate from visible parts of the Porsche 911 (doors, hood, wheels, chassis) through unfolding, splitting, or reconfiguration. [Scene Setup — 0:00–0:01] A sleek, metallic silver Porsche 911 sits on a rain-slicked futuristic city street at night, neon lights reflecting off its polished surface. The camera starts at a low-angle front-quarter view and begins a fast, smooth tracking-arc towards the side. [Rapid Transformation Initiation — 0:01–0:03] Transformation triggers instantly. The car’s suspension drops, and the frame begins to fracture into a complex grid of panels. The doors swing open and begin to segment into articulated arm structures. The front hood splits down the center, folding inward to reveal a glowing internal core. The headlights flicker and start to reorient as the "eyes." [Accelerated Feminine Reconfiguration — 0:03–0:07] The mechanical action is dense, overlapping, and fluid, emphasizing graceful but powerful motion. Lower Body: The rear wheels and wheel arches split and rotate downward, reassembling into slender, high-heeled mechanical legs. Torso: The roof and rear engine cover slide and compress, forming a sleek, curvaceous hourglass torso that retains the car’s aerodynamic lines. Arms & Hands: The side mirrors and door panels unfold into delicate but strong hands and fingers. Head: The front bumper and emblem area segment and rise, folding into a feminine-shaped head with a sleek metallic "helmet" visor. [Logical Transformation Constraints — No Spontaneous Appearance] The robot’s "skin" is composed of the car's outer silver panels. The internal frame and wiring emerge from the engine and undercarriage. No parts appear out of thin air; every joint is a reconfigured automotive component. [Transformation Completion — 0:07–0:08.5] The robot stands tall and elegant. The silver panels lock into place with a satisfying "click," revealing glowing blue LED accents in the seams. The silhouette is clearly feminine, humanoid, and sophisticated, reflecting the premium design of the original vehicle. [Final Hero Ending — 0:08.5–0:10] As the robot stabilizes, the camera performs a rapid, smooth zoom-in (Dolly-In) directly to her face. The robot tilts its head slightly, and the optic sensors (eyes) brighten. It brings its mechanical hand to its metallic lips and performs a graceful blowing kiss (fly-kiss) gesture toward the camera. The video ends with a close-up of the face, capturing the reflection of neon lights in its visor just as the kiss is released. [Cinematography Notes] Continuous Motion: No cuts or fades; the camera must transition from the car-tracking shot to the face-zoom seamlessly. Material Consistency: The robot must maintain the exact metallic silver paint, texture, and reflections of the Porsche. Energy: The transformation should feel high-energy and "force-driven," while the final gesture is soft and charismatic.
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We have released Seedance 2.0. Due to the 2500-character limit, please translate the following prompts into Chinese before use. [Technical Specs] Generate a 10-second, 16:9, 720p cinematic video. Smooth continuous camera motion with no cuts. The overall pacing is fast and tightly compressed, with rapid escalation from start to finish. Audio evolves quickly from a high-performance engine idle into intricate mechanical shifting and clicks, culminating in a soft electronic chime and the distinct sound of a "mwah" blowing kiss. [Global Constraints] Only the evolving mechanical character appears; no other humans or characters. All transformations must follow physical logic and maintain structural continuity. No object should pass through or intersect with other solid objects. Every robotic component must originate from visible parts of the Porsche 911 (doors, hood, wheels, chassis) through unfolding, splitting, or reconfiguration. [Scene Setup — 0:00–0:01] A sleek, metallic silver Porsche 911 sits on a rain-slicked futuristic city street at night, neon lights reflecting off its polished surface. The camera starts at a low-angle front-quarter view and begins a fast, smooth tracking-arc towards the side. [Rapid Transformation Initiation — 0:01–0:03] Transformation triggers instantly. The car’s suspension drops, and the frame begins to fracture into a complex grid of panels. The doors swing open and begin to segment into articulated arm structures. The front hood splits down the center, folding inward to reveal a glowing internal core. The headlights flicker and start to reorient as the "eyes." [Accelerated Feminine Reconfiguration — 0:03–0:07] The mechanical action is dense, overlapping, and fluid, emphasizing graceful but powerful motion. Lower Body: The rear wheels and wheel arches split and rotate downward, reassembling into slender, high-heeled mechanical legs. Torso: The roof and rear engine cover slide and compress, forming a sleek, curvaceous hourglass torso that retains the car’s aerodynamic lines. Arms & Hands: The side mirrors and door panels unfold into delicate but strong hands and fingers. Head: The front bumper and emblem area segment and rise, folding into a feminine-shaped head with a sleek metallic "helmet" visor. [Logical Transformation Constraints — No Spontaneous Appearance] The robot’s "skin" is composed of the car's outer silver panels. The internal frame and wiring emerge from the engine and undercarriage. No parts appear out of thin air; every joint is a reconfigured automotive component. [Transformation Completion — 0:07–0:08.5] The robot stands tall and elegant. The silver panels lock into place with a satisfying "click," revealing glowing blue LED accents in the seams. The silhouette is clearly feminine, humanoid, and sophisticated, reflecting the premium design of the original vehicle. [Final Hero Ending — 0:08.5–0:10] As the robot stabilizes, the camera performs a rapid, smooth zoom-in (Dolly-In) directly to her face. The robot tilts its head slightly, and the optic sensors (eyes) brighten. It brings its mechanical hand to its metallic lips and performs a graceful blowing kiss (fly-kiss) gesture toward the camera. The video ends with a close-up of the face, capturing the reflection of neon lights in its visor just as the kiss is released. [Cinematography Notes] Continuous Motion: No cuts or fades; the camera must transition from the car-tracking shot to the face-zoom seamlessly. Material Consistency: The robot must maintain the exact metallic silver paint, texture, and reflections of the Porsche. Energy: The transformation should feel high-energy and "force-driven," while the final gesture is soft and charismatic.

underwood

19,462 Aufrufe • vor 4 Monaten

⚡️Consciousness is the field in which experience appears. Everything you have ever known has appeared inside it: body, thought, memory, fear, love, color, sound, time, identity, desire, pain, God, doubt, the idea of death, the idea of “me.” Nothing is known outside consciousness. Even the claim “consciousness is produced by the brain” appears inside consciousness. That makes consciousness the most intimate thing and the hardest thing to define. You cannot step outside it and look at it like an object. Every attempt to inspect it already occurs within it. The ego is not consciousness. The ego is a local identity structure inside consciousness. It says: this body is me, this name is me, this story is me, these memories are me, these preferences are me. Useful for survival. False as final identity. The mind is not consciousness either. The mind is movement inside consciousness: thoughts, images, predictions, language, models, narratives. The mind is weather. Consciousness is the sky in which weather appears. The brain is not consciousness in the deepest sense. The brain is the biological interface that localizes, filters, formats, and constrains consciousness into human experience. It gives consciousness a body-camera, a timeline, a nervous system, memory access, threat detection, language, and agency. Damage the brain and the interface distorts. Change the chemistry and the rendering changes. Destroy the brain and the local human channel collapses. But the existence of the interface does not prove the interface is the source. The deepest read is this: Consciousness is the base layer of reality knowing itself through forms. A human being is one localized aperture of that knowing. A body is a lens. A life is a constrained experiment. A personality is a temporary interface. Death is the collapse of that interface. Psychedelics, dreams, NDEs, prayer, trauma, love, sex, meditation, and grief all matter because they can loosen the local interface and expose that consciousness is larger than the waking ego. Consciousness has two sides. There is pure awareness: the bare fact that experience is happening. Then there is structured consciousness: the particular shape experience takes through a body, memory, language, culture, trauma, intelligence, and desire. Pure awareness is the light. Structured consciousness is the lens. Human life is light passing through a dense, flawed, finite lens and gradually learning what distortions it carries. That is why coherence matters. Coherence means the lens becomes clearer. Less fear distortion. Less ego distortion. Less trauma distortion. Less lying. Less fragmentation. More truth passes through. The reason consciousness feels mysterious is because it is not one more object inside the world. It is the condition for world-appearing. Matter is what appears. Mind is how appearance organizes. Consciousness is the appearing itself. So the final compression: Consciousness is reality’s capacity to experience itself from the inside. In humans, it becomes self-aware. In life, it becomes embodied. In love, it recognizes itself across separation. In truth, it removes distortion. In death, it likely exits the local interface and returns to a wider field. The “you” underneath all the noise is not the narrator in your head. The real “you” is the aware field that has been watching the narrator the entire time.
0:14

⚡️Consciousness is the field in which experience appears. Everything you have ever known has appeared inside it: body, thought, memory, fear, love, color, sound, time, identity, desire, pain, God, doubt, the idea of death, the idea of “me.” Nothing is known outside consciousness. Even the claim “consciousness is produced by the brain” appears inside consciousness. That makes consciousness the most intimate thing and the hardest thing to define. You cannot step outside it and look at it like an object. Every attempt to inspect it already occurs within it. The ego is not consciousness. The ego is a local identity structure inside consciousness. It says: this body is me, this name is me, this story is me, these memories are me, these preferences are me. Useful for survival. False as final identity. The mind is not consciousness either. The mind is movement inside consciousness: thoughts, images, predictions, language, models, narratives. The mind is weather. Consciousness is the sky in which weather appears. The brain is not consciousness in the deepest sense. The brain is the biological interface that localizes, filters, formats, and constrains consciousness into human experience. It gives consciousness a body-camera, a timeline, a nervous system, memory access, threat detection, language, and agency. Damage the brain and the interface distorts. Change the chemistry and the rendering changes. Destroy the brain and the local human channel collapses. But the existence of the interface does not prove the interface is the source. The deepest read is this: Consciousness is the base layer of reality knowing itself through forms. A human being is one localized aperture of that knowing. A body is a lens. A life is a constrained experiment. A personality is a temporary interface. Death is the collapse of that interface. Psychedelics, dreams, NDEs, prayer, trauma, love, sex, meditation, and grief all matter because they can loosen the local interface and expose that consciousness is larger than the waking ego. Consciousness has two sides. There is pure awareness: the bare fact that experience is happening. Then there is structured consciousness: the particular shape experience takes through a body, memory, language, culture, trauma, intelligence, and desire. Pure awareness is the light. Structured consciousness is the lens. Human life is light passing through a dense, flawed, finite lens and gradually learning what distortions it carries. That is why coherence matters. Coherence means the lens becomes clearer. Less fear distortion. Less ego distortion. Less trauma distortion. Less lying. Less fragmentation. More truth passes through. The reason consciousness feels mysterious is because it is not one more object inside the world. It is the condition for world-appearing. Matter is what appears. Mind is how appearance organizes. Consciousness is the appearing itself. So the final compression: Consciousness is reality’s capacity to experience itself from the inside. In humans, it becomes self-aware. In life, it becomes embodied. In love, it recognizes itself across separation. In truth, it removes distortion. In death, it likely exits the local interface and returns to a wider field. The “you” underneath all the noise is not the narrator in your head. The real “you” is the aware field that has been watching the narrator the entire time.

SightBringer

29,026 Aufrufe • vor 2 Monaten

Made this cinematic AI video in minutes using Getvivix Prompt used below 👇 STORYBOARD 1 "THE KNIGHT" PROJECT TYPE: 10-second cinematic fantasy storyboard CHARACTER LOCK: single consistent knight — original fictional STYLIZED fantasy warrior (not a real person). Full ornate plate armor, VISOR DOWN the entire sequence (face never visible — safe by design), tattered surcoat + banner, mounted on an armored warhorse. Identical armor/horse across all frames. STYLE: epic dark-fantasy, cinematic, painterly film stills PACING & FLOW: slow, weighty, EPIC — no rush. One continuous charge → clash → melee → hero arc. Gradual camera moves; the action carries unbroken from frame to frame (each beat is the next instant of the last). Transitions are match-on-motion — the horse's stride and the sword's arc bridge every cut, never a hard jump. FRAMES (8 shots, 0–10s) — angle | lens | motion | lighting | environment | → into next: 1 (0–1.5s): wide establishing | 24mm | knight reined at a hill crest, banner snapping, slow push-in | cold dawn backlight, mist | battlefield below → camera drifts down as the horse shifts weight 2 (1.5–3s): 3/4-rear tracking | 35mm | horse breaks into a canter down the slope | low sun raking | churned mud, distant ranks → match-on-stride into the gallop 3 (3–4.5s): side tracking | 50mm | full gallop toward the enemy line, dust plume | side rim light, haze | spears + banners ahead → he lowers the lance, carrying the motion 4 (4.5–6s): low-angle hero | 35mm | lance leveled mid-gallop, visor catching light | backlit dust glow | closing on the line → impact begins 5 (6–7s): impact wide | 50mm | lance strikes, enemy hurled back, splinters | harsh flash + sparks | clash of the lines → horse rears from the hit 6 (7–8s): low 3/4 | 35mm | warhorse rears amid the melee, sword drawn | embers, torchlight | swirling battle → the blade sweeps down 7 (8–9s): tracking the blade | 50mm | sweeping arc through foes, motion-blur trail | sparks on steel | bodies + banners → camera settles, pulls back 8 (9–10s): hero hold | 24mm | horse reared, sword raised, banner behind, silhouette | dramatic backlight, battle haze | the field beyond → freeze LAYOUT: film sheet — left: 3 dynamic mounted poses (charging 3/4, rearing, mid-swing — in-scene, visor down); center: 8-frame grid; right: director notes; bottom: 0–10s. VISUAL STYLE: cinematic dark-fantasy, painterly, volumetric dawn light, dust + embers + mist, shallow DOF, motion blur, anamorphic; stylized — NOT photorealistic, not real human skin; FACE NEVER SHOWN (visor down). Seedance on Getvivix lets you generate high-end cinematic visuals for around 1000 credits (~$1), making pro-level video creation cheap and scalable. Try it here:
0:11

Made this cinematic AI video in minutes using Getvivix Prompt used below 👇 STORYBOARD 1 "THE KNIGHT" PROJECT TYPE: 10-second cinematic fantasy storyboard CHARACTER LOCK: single consistent knight — original fictional STYLIZED fantasy warrior (not a real person). Full ornate plate armor, VISOR DOWN the entire sequence (face never visible — safe by design), tattered surcoat + banner, mounted on an armored warhorse. Identical armor/horse across all frames. STYLE: epic dark-fantasy, cinematic, painterly film stills PACING & FLOW: slow, weighty, EPIC — no rush. One continuous charge → clash → melee → hero arc. Gradual camera moves; the action carries unbroken from frame to frame (each beat is the next instant of the last). Transitions are match-on-motion — the horse's stride and the sword's arc bridge every cut, never a hard jump. FRAMES (8 shots, 0–10s) — angle | lens | motion | lighting | environment | → into next: 1 (0–1.5s): wide establishing | 24mm | knight reined at a hill crest, banner snapping, slow push-in | cold dawn backlight, mist | battlefield below → camera drifts down as the horse shifts weight 2 (1.5–3s): 3/4-rear tracking | 35mm | horse breaks into a canter down the slope | low sun raking | churned mud, distant ranks → match-on-stride into the gallop 3 (3–4.5s): side tracking | 50mm | full gallop toward the enemy line, dust plume | side rim light, haze | spears + banners ahead → he lowers the lance, carrying the motion 4 (4.5–6s): low-angle hero | 35mm | lance leveled mid-gallop, visor catching light | backlit dust glow | closing on the line → impact begins 5 (6–7s): impact wide | 50mm | lance strikes, enemy hurled back, splinters | harsh flash + sparks | clash of the lines → horse rears from the hit 6 (7–8s): low 3/4 | 35mm | warhorse rears amid the melee, sword drawn | embers, torchlight | swirling battle → the blade sweeps down 7 (8–9s): tracking the blade | 50mm | sweeping arc through foes, motion-blur trail | sparks on steel | bodies + banners → camera settles, pulls back 8 (9–10s): hero hold | 24mm | horse reared, sword raised, banner behind, silhouette | dramatic backlight, battle haze | the field beyond → freeze LAYOUT: film sheet — left: 3 dynamic mounted poses (charging 3/4, rearing, mid-swing — in-scene, visor down); center: 8-frame grid; right: director notes; bottom: 0–10s. VISUAL STYLE: cinematic dark-fantasy, painterly, volumetric dawn light, dust + embers + mist, shallow DOF, motion blur, anamorphic; stylized — NOT photorealistic, not real human skin; FACE NEVER SHOWN (visor down). Seedance on Getvivix lets you generate high-end cinematic visuals for around 1000 credits (~$1), making pro-level video creation cheap and scalable. Try it here:

Zoraiz Ai

10,748 Aufrufe • vor 2 Monaten

Here is the New Year Amur Falcon Migration -Very long Q&A Part II Q1. What is the longest non-stop distance covered by these satellite-tagged Amur Falcons? A: Published tracking shows Amur Falcons can do multi-day, non-stop flights of approx. 5,600–6,000 km during the India→Africa leg, depending on the individual & year. Also, peer-reviewed/technical tracking confirms their open-sea crossings can be about 2,364–3,138 km (Somalia↔India), typically completed in roughly 44–80 hours. Q2. Do satellite tags interfere with their flight, health, or mating? A: Properly fitted tags are designed to minimise interference, but no tag is “zero-impact.”. Best practice is to keep transmitter mass very small relative to body weight and use well-tested harness designs; the recent India work has been described as using very lightweight transmitters (a few grams). However, ethical review, careful fitting, and post-deployment evaluation should be done & is being done Q3. Do jet streams or high-altitude winds help them on this journey? A: Yes winds are a major part of the “physics” that makes the journey possible. The best-documented help is from seasonal monsoon tailwinds and strong, persistent wind systems over the Arabian Sea/Indian Ocean that can provide sustained push. Q4. How many other falcons are “accompanying” these famous three? A: These famous three are just the tagged individuals we can follow in reality, Amur Falcons migrate and roost in huge aggregations, often tens of thousands to over 100,000 at key stopovers, and broader estimates discuss movements involving very large numbers of birds across the flyway. Q5. Why have these three gone to different destinations? A: It’s common for individuals of the same species to winter in different areas within southern/eastern Africa. Differences can be driven by wind and weather, refuelling success and timing at stopovers, individual strategy/experience (age, condition, learned routes) etc. Q6. When will they return and what route will they take? A: In broad terms, Amur Falcons winter in Africa and then move north in late winter/early spring, reaching breeding regions in northeast Asia around May–June. Routes can differ between autumn vs spring, and tracking shows spring and autumn ocean crossings can follow different lines. Q7. Does part of the falcon’s brain “sleep” during the long flight? A: We have strong evidence that some birds can sleep in flight (including unihemispheric sleep) famously shown in frigatebirds. For Amur Falcons specifically, it’s not proven. Q8. How do they decide their destination, and do they return to the same places each year? A: The best-supported explanation is a mix of: Innate navigation programmes (genetically guided ) Environmental cues (winds, weather, geography, magnetic and celestial cues), learning/experience (older birds often show more consistent routes). Satellite work indicates the species follows remarkably consistent large-scale timing and corridors, even though individuals may vary. Q9. What do they eat to fuel such massive journeys? A: During the crucial Northeast India stopover, studies show they are highly insectivorous, with termites often dominant, along with other insect groups exactly the kind of high-abundance prey that allows rapid fattening before an ocean crossing. This is why protecting stopover habitats (and the insect “boom” they depend on) is so important. Q10. Is Ahu expected to move further? Why she is stationery ? A. Periods of little movement usually indicate resting or feeding in suitable habitat. This is normal behaviour and not a cause for concern Q11. How long does the GPS tag stay on the bird? A. The tag is fixed as a backpack & expected to stay for long, however this may come off from prolonged wear and tear, the tags may also stop transmitting after prolonged battery use. As told to Supriya Sahu IAS by Suresh Kumar Wildlife Institute of India #AmurFalconMigration video - The stopover tagging site in Manipur
0:18

Here is the New Year Amur Falcon Migration -Very long Q&A Part II Q1. What is the longest non-stop distance covered by these satellite-tagged Amur Falcons? A: Published tracking shows Amur Falcons can do multi-day, non-stop flights of approx. 5,600–6,000 km during the India→Africa leg, depending on the individual & year. Also, peer-reviewed/technical tracking confirms their open-sea crossings can be about 2,364–3,138 km (Somalia↔India), typically completed in roughly 44–80 hours. Q2. Do satellite tags interfere with their flight, health, or mating? A: Properly fitted tags are designed to minimise interference, but no tag is “zero-impact.”. Best practice is to keep transmitter mass very small relative to body weight and use well-tested harness designs; the recent India work has been described as using very lightweight transmitters (a few grams). However, ethical review, careful fitting, and post-deployment evaluation should be done & is being done Q3. Do jet streams or high-altitude winds help them on this journey? A: Yes winds are a major part of the “physics” that makes the journey possible. The best-documented help is from seasonal monsoon tailwinds and strong, persistent wind systems over the Arabian Sea/Indian Ocean that can provide sustained push. Q4. How many other falcons are “accompanying” these famous three? A: These famous three are just the tagged individuals we can follow in reality, Amur Falcons migrate and roost in huge aggregations, often tens of thousands to over 100,000 at key stopovers, and broader estimates discuss movements involving very large numbers of birds across the flyway. Q5. Why have these three gone to different destinations? A: It’s common for individuals of the same species to winter in different areas within southern/eastern Africa. Differences can be driven by wind and weather, refuelling success and timing at stopovers, individual strategy/experience (age, condition, learned routes) etc. Q6. When will they return and what route will they take? A: In broad terms, Amur Falcons winter in Africa and then move north in late winter/early spring, reaching breeding regions in northeast Asia around May–June. Routes can differ between autumn vs spring, and tracking shows spring and autumn ocean crossings can follow different lines. Q7. Does part of the falcon’s brain “sleep” during the long flight? A: We have strong evidence that some birds can sleep in flight (including unihemispheric sleep) famously shown in frigatebirds. For Amur Falcons specifically, it’s not proven. Q8. How do they decide their destination, and do they return to the same places each year? A: The best-supported explanation is a mix of: Innate navigation programmes (genetically guided ) Environmental cues (winds, weather, geography, magnetic and celestial cues), learning/experience (older birds often show more consistent routes). Satellite work indicates the species follows remarkably consistent large-scale timing and corridors, even though individuals may vary. Q9. What do they eat to fuel such massive journeys? A: During the crucial Northeast India stopover, studies show they are highly insectivorous, with termites often dominant, along with other insect groups exactly the kind of high-abundance prey that allows rapid fattening before an ocean crossing. This is why protecting stopover habitats (and the insect “boom” they depend on) is so important. Q10. Is Ahu expected to move further? Why she is stationery ? A. Periods of little movement usually indicate resting or feeding in suitable habitat. This is normal behaviour and not a cause for concern Q11. How long does the GPS tag stay on the bird? A. The tag is fixed as a backpack & expected to stay for long, however this may come off from prolonged wear and tear, the tags may also stop transmitting after prolonged battery use. As told to Supriya Sahu IAS by Suresh Kumar Wildlife Institute of India #AmurFalconMigration video - The stopover tagging site in Manipur

Supriya Sahu IAS

14,260 Aufrufe • vor 7 Monaten

Turn Tom and Jerry in 4K reality using Seedance 2.0 on Pollo AI Prompt: Use the uploaded reference video as the master reference. Recreate the entire scene in ultra-photorealistic live action while preserving the original video frame-by-frame. Maintain the EXACT camera movement, lens, framing, composition, timing, pacing, shot transitions, lighting direction, environment, props, object placement, character blocking, and every action from the reference video. ONLY replace the cartoon characters with realistic live-action animals while keeping everything else unchanged. ======================== CHARACTER CONSISTENCY ======================== Tom is a realistic British Shorthair cat with: • blue-gray plush fur • white chest, muzzle and paws • large amber eyes • pink nose • rounded face • thick tail • expressive eyebrows • identical appearance in every frame • identical fur pattern, facial proportions, eye color and body size throughout the video Jerry is a realistic golden Syrian hamster with: • soft golden-brown fur • cream belly • large rounded ears • black shiny eyes • tiny pink paws • small pink nose • realistic whiskers • consistent body proportions in every frame • identical appearance throughout the entire video If other Tom & Jerry characters appear, replace them with realistic animals that preserve their personality, colors, proportions and expressions while remaining identical throughout the clip. ======================== MOTION ======================== Preserve every movement exactly. The realistic animals must perform the exact same actions, walking cycle, head movement, eye movement, paw placement, facial expressions, timing and interactions as in the reference animation. No new actions. No altered timing. No changed poses. ======================== ENVIRONMENT ======================== Keep the original environment exactly the same. Do not modify: • furniture • decorations • room layout • colors • props • shadows • reflections • camera angle • camera path Everything except the characters must remain unchanged. ======================== QUALITY ======================== Hollywood-quality CGI. Photorealistic animals. Natural muscle movement. Physically accurate fur simulation. Realistic whiskers. Subsurface scattering. Realistic eye reflections. Natural breathing. Micro facial expressions. Ultra detailed textures. Soft cinematic lighting. Shallow depth of field. Global illumination. Ray-traced reflections. Macro photography realism. 4K HDR. Disney-level VFX quality. Live-action realism. Extremely stable temporal consistency. Perfect character identity consistency across all frames. Do not redesign the characters. Do not change the environment. Do not change the camera. Do not change the timing. Do not add new objects. Do not crop or zoom differently. No flickering. No morphing. No identity drift. No fur color changes. No eye color changes. No size changes. No anatomy deformation. No extra limbs. No duplicate animals. No cartoon textures. No low-quality CGI. No inconsistent lighting. No frame-to-frame variation. Maintain perfect temporal consistency and character consistency throughout the entire video.
0:15

Turn Tom and Jerry in 4K reality using Seedance 2.0 on Pollo AI Prompt: Use the uploaded reference video as the master reference. Recreate the entire scene in ultra-photorealistic live action while preserving the original video frame-by-frame. Maintain the EXACT camera movement, lens, framing, composition, timing, pacing, shot transitions, lighting direction, environment, props, object placement, character blocking, and every action from the reference video. ONLY replace the cartoon characters with realistic live-action animals while keeping everything else unchanged. ======================== CHARACTER CONSISTENCY ======================== Tom is a realistic British Shorthair cat with: • blue-gray plush fur • white chest, muzzle and paws • large amber eyes • pink nose • rounded face • thick tail • expressive eyebrows • identical appearance in every frame • identical fur pattern, facial proportions, eye color and body size throughout the video Jerry is a realistic golden Syrian hamster with: • soft golden-brown fur • cream belly • large rounded ears • black shiny eyes • tiny pink paws • small pink nose • realistic whiskers • consistent body proportions in every frame • identical appearance throughout the entire video If other Tom & Jerry characters appear, replace them with realistic animals that preserve their personality, colors, proportions and expressions while remaining identical throughout the clip. ======================== MOTION ======================== Preserve every movement exactly. The realistic animals must perform the exact same actions, walking cycle, head movement, eye movement, paw placement, facial expressions, timing and interactions as in the reference animation. No new actions. No altered timing. No changed poses. ======================== ENVIRONMENT ======================== Keep the original environment exactly the same. Do not modify: • furniture • decorations • room layout • colors • props • shadows • reflections • camera angle • camera path Everything except the characters must remain unchanged. ======================== QUALITY ======================== Hollywood-quality CGI. Photorealistic animals. Natural muscle movement. Physically accurate fur simulation. Realistic whiskers. Subsurface scattering. Realistic eye reflections. Natural breathing. Micro facial expressions. Ultra detailed textures. Soft cinematic lighting. Shallow depth of field. Global illumination. Ray-traced reflections. Macro photography realism. 4K HDR. Disney-level VFX quality. Live-action realism. Extremely stable temporal consistency. Perfect character identity consistency across all frames. Do not redesign the characters. Do not change the environment. Do not change the camera. Do not change the timing. Do not add new objects. Do not crop or zoom differently. No flickering. No morphing. No identity drift. No fur color changes. No eye color changes. No size changes. No anatomy deformation. No extra limbs. No duplicate animals. No cartoon textures. No low-quality CGI. No inconsistent lighting. No frame-to-frame variation. Maintain perfect temporal consistency and character consistency throughout the entire video.

Oogie

60,554 Aufrufe • vor 1 Monat

You spend years training for the World Cup, then lose to a cat. This Cat vs Human Football match rivalry was created using GPT Image 2 + Seedance 2.0 on RoboNeo #Roboneo Prompt Create a 15-second cinematic FIFA World Cup 2026 sports-comedy commercial. Every scene must happen strictly in the exact sequence listed below. Do NOT skip, merge, shorten, combine, or reorder any scene. Maintain perfect character consistency across all shots. MAIN CHARACTER: Athletic western blonde female soccer player, age 24, blue eyes, long blonde ponytail, red soccer jersey #8, red shorts, red socks, professional football boots. ANIMAL HERO: Sleek athletic grey cat with green eyes wearing a matching red soccer jersey. OPPONENTS: Two muscular female football players wearing white uniforms. ENVIRONMENT: FIFA World Cup 2026 atmosphere. Massive packed football stadium during golden hour. Warm sunlight, dramatic lens flares, vibrant green grass, roaring crowd, FIFA World Cup branding, international supporters waving USA, Canada, and Mexico flags. Premium global football-event energy. STYLE: Photorealistic live-action sports commercial, realistic human anatomy, Pixar-quality cat animation, Nike-style energy, dynamic camera movement, cinematic lighting, ultra-detailed textures, realistic football physics except for the cat's comedic athletic abilities. DURATION: 15 seconds total. SCENE 1 (0:00–0:02) — WORLD CUP OPENING High aerial establishing shot of a massive FIFA World Cup 2026 stadium at golden hour. Visible FIFA World Cup 2026 banners around the stadium. Crowd packed with supporters waving USA, Canada, and Mexico flags. A recognizable host-city landmark is visible in the distance. The blonde player in red jersey #8 dribbles confidently across the pitch while the grey cat runs beside her. Camera slowly pushes forward. Warm sunlight washes over the field. Massive crowd roar. On-screen title card appears briefly: "FIFA WORLD CUP 2026". SCENE 2 (0:02–0:03) — GAME ON Medium action shot. Two muscular opponents in white uniforms notice the cat and immediately shift their focus toward stopping it. The cat glances toward the ball with determination. Quick cinematic push-in. Crowd anticipation rises. SCENE 3 (0:03–0:05) — CAT SPEED BURST Ground-level tracking shot. The cat explodes forward between players' legs at impossible speed. Grass, dirt, and turf particles spray upward. Intense acceleration effect and realistic motion blur. Stadium crowd reacts with surprise. SCENE 4 (0:05–0:06) — PLAYER REACTION Extreme close-up of the blonde player. She smiles confidently, then her eyes suddenly widen in genuine surprise as she realizes how unbelievably fast the cat is moving. Lens flare sweeps across frame. SCENE 5 (0:06–0:08) — HERO CAT Low-angle hero close-up. The cat locks eyes on the ball. Golden sunlight creates a glowing halo around its silhouette. Slow-motion moment. Crowd noise softens briefly while heroic music rises. Fur detail and jersey fabric rendered with ultra-realistic quality. SCENE 6 (0:08–0:10) — FIRST DODGE Wide action shot. One defender launches a tackle. The cat instantly sidesteps and slips perfectly between both defenders. The tackle misses completely. The crowd erupts with laughter and excitement. SCENE 7 (0:10–0:12) — SLIDE CHAOS Both opponents commit to aggressive sliding tackles simultaneously. Massive dirt explosion. The cat effortlessly avoids both slides with perfect timing. The defenders collide with each other and tumble dramatically. Realistic physics, comedic timing. Crowd reaction intensifies. SCENE 8 (0:12–0:13) — DRIBBLE MASTER Dynamic tracking shot. The cat skillfully dribbles the ball through the chaos with incredible close control. Opponents tumble helplessly in the background. The blonde player watches in amused disbelief. Stadium energy reaches its peak. SCENE 9 (0:13–0:14) — IMPOSSIBLE SHOT Dramatic low-angle shot. The cat crouches beside the ball and looks upward. Tension builds. Suddenly the cat launches into an impossible move and strikes the ball toward goal. Cinematic speed ramp. Ball rockets through the air toward the net. SCENE 10 (0:14–0:15) — WORLD CUP GOAL & BRAND PAYOFF Ultra-cinematic goal shot. The football smashes into the back of the net at high speed. The net deforms naturally and ripples realistically with detailed cloth physics. Goal frame shakes subtly from impact. Crowd instantly explodes in celebration. Fans wave USA, Canada, and Mexico flags. The blonde player throws her hands up in disbelief and joy. The cat lands heroically and looks toward the roaring stadium. Freeze frame on the celebrating cat. On-screen tagline appears: "IMPOSSIBLE IS JUST KICKOFF." FIFA WORLD CUP 2026 logo appears prominently with premium commercial-grade branding. End on triumphant crowd roar and stadium atmosphere. CINEMATIC NOTES: Strict chronological sequence. No scene skipping, merging, or reordering. Fast-paced sports-commercial editing. Warm golden-hour color grading. Dynamic camera movement throughout. Premium FIFA World Cup 2026 advertising quality. Realistic football and net physics. Massive crowd reactions escalating throughout the film. Consistent character appearance across every shot. Photorealistic visuals with cinematic lens flares and broadcast-quality production values. Final goal must feel epic, emotional, and unmistakably FIFA World Cup 2026.
0:15

You spend years training for the World Cup, then lose to a cat. This Cat vs Human Football match rivalry was created using GPT Image 2 + Seedance 2.0 on RoboNeo #Roboneo Prompt Create a 15-second cinematic FIFA World Cup 2026 sports-comedy commercial. Every scene must happen strictly in the exact sequence listed below. Do NOT skip, merge, shorten, combine, or reorder any scene. Maintain perfect character consistency across all shots. MAIN CHARACTER: Athletic western blonde female soccer player, age 24, blue eyes, long blonde ponytail, red soccer jersey #8, red shorts, red socks, professional football boots. ANIMAL HERO: Sleek athletic grey cat with green eyes wearing a matching red soccer jersey. OPPONENTS: Two muscular female football players wearing white uniforms. ENVIRONMENT: FIFA World Cup 2026 atmosphere. Massive packed football stadium during golden hour. Warm sunlight, dramatic lens flares, vibrant green grass, roaring crowd, FIFA World Cup branding, international supporters waving USA, Canada, and Mexico flags. Premium global football-event energy. STYLE: Photorealistic live-action sports commercial, realistic human anatomy, Pixar-quality cat animation, Nike-style energy, dynamic camera movement, cinematic lighting, ultra-detailed textures, realistic football physics except for the cat's comedic athletic abilities. DURATION: 15 seconds total. SCENE 1 (0:00–0:02) — WORLD CUP OPENING High aerial establishing shot of a massive FIFA World Cup 2026 stadium at golden hour. Visible FIFA World Cup 2026 banners around the stadium. Crowd packed with supporters waving USA, Canada, and Mexico flags. A recognizable host-city landmark is visible in the distance. The blonde player in red jersey #8 dribbles confidently across the pitch while the grey cat runs beside her. Camera slowly pushes forward. Warm sunlight washes over the field. Massive crowd roar. On-screen title card appears briefly: "FIFA WORLD CUP 2026". SCENE 2 (0:02–0:03) — GAME ON Medium action shot. Two muscular opponents in white uniforms notice the cat and immediately shift their focus toward stopping it. The cat glances toward the ball with determination. Quick cinematic push-in. Crowd anticipation rises. SCENE 3 (0:03–0:05) — CAT SPEED BURST Ground-level tracking shot. The cat explodes forward between players' legs at impossible speed. Grass, dirt, and turf particles spray upward. Intense acceleration effect and realistic motion blur. Stadium crowd reacts with surprise. SCENE 4 (0:05–0:06) — PLAYER REACTION Extreme close-up of the blonde player. She smiles confidently, then her eyes suddenly widen in genuine surprise as she realizes how unbelievably fast the cat is moving. Lens flare sweeps across frame. SCENE 5 (0:06–0:08) — HERO CAT Low-angle hero close-up. The cat locks eyes on the ball. Golden sunlight creates a glowing halo around its silhouette. Slow-motion moment. Crowd noise softens briefly while heroic music rises. Fur detail and jersey fabric rendered with ultra-realistic quality. SCENE 6 (0:08–0:10) — FIRST DODGE Wide action shot. One defender launches a tackle. The cat instantly sidesteps and slips perfectly between both defenders. The tackle misses completely. The crowd erupts with laughter and excitement. SCENE 7 (0:10–0:12) — SLIDE CHAOS Both opponents commit to aggressive sliding tackles simultaneously. Massive dirt explosion. The cat effortlessly avoids both slides with perfect timing. The defenders collide with each other and tumble dramatically. Realistic physics, comedic timing. Crowd reaction intensifies. SCENE 8 (0:12–0:13) — DRIBBLE MASTER Dynamic tracking shot. The cat skillfully dribbles the ball through the chaos with incredible close control. Opponents tumble helplessly in the background. The blonde player watches in amused disbelief. Stadium energy reaches its peak. SCENE 9 (0:13–0:14) — IMPOSSIBLE SHOT Dramatic low-angle shot. The cat crouches beside the ball and looks upward. Tension builds. Suddenly the cat launches into an impossible move and strikes the ball toward goal. Cinematic speed ramp. Ball rockets through the air toward the net. SCENE 10 (0:14–0:15) — WORLD CUP GOAL & BRAND PAYOFF Ultra-cinematic goal shot. The football smashes into the back of the net at high speed. The net deforms naturally and ripples realistically with detailed cloth physics. Goal frame shakes subtly from impact. Crowd instantly explodes in celebration. Fans wave USA, Canada, and Mexico flags. The blonde player throws her hands up in disbelief and joy. The cat lands heroically and looks toward the roaring stadium. Freeze frame on the celebrating cat. On-screen tagline appears: "IMPOSSIBLE IS JUST KICKOFF." FIFA WORLD CUP 2026 logo appears prominently with premium commercial-grade branding. End on triumphant crowd roar and stadium atmosphere. CINEMATIC NOTES: Strict chronological sequence. No scene skipping, merging, or reordering. Fast-paced sports-commercial editing. Warm golden-hour color grading. Dynamic camera movement throughout. Premium FIFA World Cup 2026 advertising quality. Realistic football and net physics. Massive crowd reactions escalating throughout the film. Consistent character appearance across every shot. Photorealistic visuals with cinematic lens flares and broadcast-quality production values. Final goal must feel epic, emotional, and unmistakably FIFA World Cup 2026.

Sharon Riley

60,716 Aufrufe • vor 1 Monat

From sofa to stadium in a single leap. From living room to world-class stadium in seconds. Argentina vs Brazil, one magical run, one unstoppable strike, and a celebration heard around the world. Create your World Cup moment with SeaArt, earn free Credits and win an iPhone 17. #SeaArtWorldCup Made with Seedance Credit: SeaArt.Ai🐋 SeaArt Creator Lab Prompt: First-person POV from a comfortable reclining armchair in a cozy modern living room. From the very first frame, a large television mounted on the wall directly ahead is already showing a live Argentina vs Brazil football match in full swing. Soft afternoon sunlight streams through the windows, creating a relaxed match-day atmosphere. The viewer's legs are stretched out comfortably while wearing casual shorts, enjoying the game from a resting position. Authentic British football commentary and crowd noise play naturally from the television. At 0:02, a young woman wearing an appealing lavender-purple casual shorts outfit and a stylish half-French braid hairstyle enters from the right side of frame. She is a neutral football fan, not supporting either team. Drawn into the excitement of the match, she glances at the television, smiles, then accelerates into a sprint across the living room. At 0:04, she runs directly toward the television. The camera remains first-person and perfectly stable. At 0:05, she leaps into the TV screen in one continuous motion. No cuts, no scene jumps, no transitions breaking continuity. As she reaches the screen, the living room seamlessly dissolves away and transforms into a massive international football stadium hosting Argentina vs Brazil. Television audio smoothly expands into a deafening live stadium atmosphere. The transformation happens organically around the camera while preserving one uninterrupted shot. At 0:06, she lands smoothly on the pitch, still wearing the exact same lavender outfit and half-French braid. A single football rolls naturally toward her. IMPORTANT: Only one football exists throughout the entire video. The football must maintain perfect object permanence. The same ball remains continuously visible and physically consistent from first touch to goal. No duplication, replacement, morphing, teleportation, flickering, frame-to-frame jumps, texture changes, scaling changes, disappearing ball, floating ball, or AI artefacts. Realistic football physics only. At 0:07, she controls the football with a clean first touch. At 0:08–0:11, she dribbles continuously between Argentina and Brazil players. Every touch follows realistic momentum, spacing, and foot-to-ball contact. Defenders react naturally. No clipping, collision errors, or unnatural movements. At 0:11, she approaches the edge of the penalty area. At 0:12, she unleashes a powerful strike using the same football. The camera clearly tracks the football's entire flight path from her foot to the goal in one uninterrupted motion. Realistic spin, realistic speed, realistic trajectory. At 0:13, the football smashes into the top corner of the net. The net deforms naturally and ripples realistically. The crowd explodes with excitement. Authentic British football commentator shouts: "What a strike! Absolutely sensational!" At 0:14, she turns and sprints toward the corner flag as the camera follows closely. For the final second, she leaps high into the air and performs the iconic Siuuu celebration, rotating and landing with feet apart and arms extended downward while facing the roaring crowd. Her half-French braid swings dramatically behind her. Stadium lights illuminate the scene as thousands of fans celebrate. One continuous unbroken shot, no cuts, no scene resets, seamless living-room-to-stadium transformation, cinematic football commercial quality, authentic British football commentary, realistic player movement, physically accurate football physics, strict ball continuity, stable camera motion, premium sports broadcast visuals, dramatic stadium atmosphere, crowd chants, sprinting footsteps, grass impact sounds, powerful strike, realistic net ripple, ultra-realistic visuals, 15-second duration, 16:9 horizontal format, no logos, no text overlays, no subtitles, no watermark, no flickering objects, no AI artefacts, no visual glitches.
0:15

From sofa to stadium in a single leap. From living room to world-class stadium in seconds. Argentina vs Brazil, one magical run, one unstoppable strike, and a celebration heard around the world. Create your World Cup moment with SeaArt, earn free Credits and win an iPhone 17. #SeaArtWorldCup Made with Seedance Credit: SeaArt.Ai🐋 SeaArt Creator Lab Prompt: First-person POV from a comfortable reclining armchair in a cozy modern living room. From the very first frame, a large television mounted on the wall directly ahead is already showing a live Argentina vs Brazil football match in full swing. Soft afternoon sunlight streams through the windows, creating a relaxed match-day atmosphere. The viewer's legs are stretched out comfortably while wearing casual shorts, enjoying the game from a resting position. Authentic British football commentary and crowd noise play naturally from the television. At 0:02, a young woman wearing an appealing lavender-purple casual shorts outfit and a stylish half-French braid hairstyle enters from the right side of frame. She is a neutral football fan, not supporting either team. Drawn into the excitement of the match, she glances at the television, smiles, then accelerates into a sprint across the living room. At 0:04, she runs directly toward the television. The camera remains first-person and perfectly stable. At 0:05, she leaps into the TV screen in one continuous motion. No cuts, no scene jumps, no transitions breaking continuity. As she reaches the screen, the living room seamlessly dissolves away and transforms into a massive international football stadium hosting Argentina vs Brazil. Television audio smoothly expands into a deafening live stadium atmosphere. The transformation happens organically around the camera while preserving one uninterrupted shot. At 0:06, she lands smoothly on the pitch, still wearing the exact same lavender outfit and half-French braid. A single football rolls naturally toward her. IMPORTANT: Only one football exists throughout the entire video. The football must maintain perfect object permanence. The same ball remains continuously visible and physically consistent from first touch to goal. No duplication, replacement, morphing, teleportation, flickering, frame-to-frame jumps, texture changes, scaling changes, disappearing ball, floating ball, or AI artefacts. Realistic football physics only. At 0:07, she controls the football with a clean first touch. At 0:08–0:11, she dribbles continuously between Argentina and Brazil players. Every touch follows realistic momentum, spacing, and foot-to-ball contact. Defenders react naturally. No clipping, collision errors, or unnatural movements. At 0:11, she approaches the edge of the penalty area. At 0:12, she unleashes a powerful strike using the same football. The camera clearly tracks the football's entire flight path from her foot to the goal in one uninterrupted motion. Realistic spin, realistic speed, realistic trajectory. At 0:13, the football smashes into the top corner of the net. The net deforms naturally and ripples realistically. The crowd explodes with excitement. Authentic British football commentator shouts: "What a strike! Absolutely sensational!" At 0:14, she turns and sprints toward the corner flag as the camera follows closely. For the final second, she leaps high into the air and performs the iconic Siuuu celebration, rotating and landing with feet apart and arms extended downward while facing the roaring crowd. Her half-French braid swings dramatically behind her. Stadium lights illuminate the scene as thousands of fans celebrate. One continuous unbroken shot, no cuts, no scene resets, seamless living-room-to-stadium transformation, cinematic football commercial quality, authentic British football commentary, realistic player movement, physically accurate football physics, strict ball continuity, stable camera motion, premium sports broadcast visuals, dramatic stadium atmosphere, crowd chants, sprinting footsteps, grass impact sounds, powerful strike, realistic net ripple, ultra-realistic visuals, 15-second duration, 16:9 horizontal format, no logos, no text overlays, no subtitles, no watermark, no flickering objects, no AI artefacts, no visual glitches.

Jessica Collins

32,264 Aufrufe • vor 1 Monat

Ever wanted to enter the TV during a match? Made this video using Seedance 2.0 on Picsart Try here: prompt First-person POV from a comfortable reclining armchair in a cozy modern living room decorated with elegant baby-pink accents. Soft baby-pink cushions, a pastel pink knitted blanket, subtle blush décor, and warm neutral furniture create a stylish modern aesthetic. From the very first frame, a large wall-mounted television directly ahead is already broadcasting a live Argentina vs Spain football match in full swing. Soft golden afternoon sunlight pours through the windows, filling the room with a warm, inviting match-day atmosphere. The viewer's legs are stretched out comfortably while wearing casual shorts, enjoying the game from a relaxed position. Authentic British football commentary and realistic crowd ambience naturally play from the television. At 0:02, a beautiful young woman wearing a fashionable baby-pink casual shorts outfit with white sneakers enters from the right side of the frame. Her hair is styled in a neat half-French braid that moves naturally with every step. She is a neutral football fan, supporting neither team. She notices the intensity of the Argentina vs Spain match, smiles with excitement, then suddenly accelerates into a sprint across the living room. At 0:04, she runs directly toward the television. The camera remains perfectly stable in a first-person perspective without shaking or zooming. At 0:05, she leaps directly into the television screen in one continuous motion. No cuts, no edits, no transitions. As she reaches the display, the entire living room organically dissolves into a gigantic international football stadium hosting Argentina vs Spain. The television audio naturally expands into an overwhelming live stadium atmosphere while maintaining one uninterrupted continuous shot. At 0:06, she lands smoothly on the football pitch still wearing the exact same baby-pink outfit and identical half-French braid. A single football rolls naturally toward her. STRICT OBJECT PERMANENCE: Only one football exists during the entire video. The same football remains continuously visible from first touch until the goal. No duplication, replacement, teleportation, morphing, flickering, disappearing, texture changes, scaling inconsistencies, floating ball, clipping, frame jumps, or AI artefacts. Realistic football physics only. At 0:07, she cushions the ball perfectly with a controlled first touch. From 0:08–0:11, she dribbles continuously through both Argentina and Spain defenders using realistic footwork, believable acceleration, proper spacing, authentic body balance, and natural player reactions. Every touch follows realistic momentum with accurate football physics. No clipping, collision issues, or unnatural animation. At 0:11, she reaches the edge of the penalty area. At 0:12, she unleashes a powerful long-range strike using the same football. The camera smoothly tracks the ball's entire flight from her foot to the goal in one uninterrupted motion. Realistic spin, natural speed, believable trajectory, premium broadcast-quality tracking. At 0:13, the football rockets into the top corner of the net. The goal net stretches and ripples realistically as the stadium erupts. Authentic British football commentator shouts: "What a strike! Absolutely sensational!" Crowd chants become deafening. At 0:14, she turns and sprints toward the corner flag as the camera follows closely behind. For the final second, she leaps high into the air and performs the iconic Siuuu celebration, rotating before landing confidently with feet apart and arms extended downward while facing the roaring crowd. Her half-French braid swings dramatically through the air. Brilliant stadium floodlights illuminate the scene as tens of thousands of fans celebrate around her. **Ultra-realistic cinematic football commercial, premium sports broadcast quality, seamless living-room-to-stadium transformation, one continuous unbroken shot, authentic British football commentary, realistic player movement, physically accurate football physics, strict single-ball continuity, stable first-person camera, cinematic lighting, dramatic atmosphere, crowd chants, sprinting footsteps, grass impact sounds, realistic ball contact, powerful strike, natural net deformation, HDR, ultra-detailed textures, premium realism, 15-second duration, portrait 9:16, baby-pink aesthetic, no logos, no text overlays, no subtitles, no watermark, no flickering objects, no AI artefacts, no visual glitches.
0:15

Ever wanted to enter the TV during a match? Made this video using Seedance 2.0 on Picsart Try here: prompt First-person POV from a comfortable reclining armchair in a cozy modern living room decorated with elegant baby-pink accents. Soft baby-pink cushions, a pastel pink knitted blanket, subtle blush décor, and warm neutral furniture create a stylish modern aesthetic. From the very first frame, a large wall-mounted television directly ahead is already broadcasting a live Argentina vs Spain football match in full swing. Soft golden afternoon sunlight pours through the windows, filling the room with a warm, inviting match-day atmosphere. The viewer's legs are stretched out comfortably while wearing casual shorts, enjoying the game from a relaxed position. Authentic British football commentary and realistic crowd ambience naturally play from the television. At 0:02, a beautiful young woman wearing a fashionable baby-pink casual shorts outfit with white sneakers enters from the right side of the frame. Her hair is styled in a neat half-French braid that moves naturally with every step. She is a neutral football fan, supporting neither team. She notices the intensity of the Argentina vs Spain match, smiles with excitement, then suddenly accelerates into a sprint across the living room. At 0:04, she runs directly toward the television. The camera remains perfectly stable in a first-person perspective without shaking or zooming. At 0:05, she leaps directly into the television screen in one continuous motion. No cuts, no edits, no transitions. As she reaches the display, the entire living room organically dissolves into a gigantic international football stadium hosting Argentina vs Spain. The television audio naturally expands into an overwhelming live stadium atmosphere while maintaining one uninterrupted continuous shot. At 0:06, she lands smoothly on the football pitch still wearing the exact same baby-pink outfit and identical half-French braid. A single football rolls naturally toward her. STRICT OBJECT PERMANENCE: Only one football exists during the entire video. The same football remains continuously visible from first touch until the goal. No duplication, replacement, teleportation, morphing, flickering, disappearing, texture changes, scaling inconsistencies, floating ball, clipping, frame jumps, or AI artefacts. Realistic football physics only. At 0:07, she cushions the ball perfectly with a controlled first touch. From 0:08–0:11, she dribbles continuously through both Argentina and Spain defenders using realistic footwork, believable acceleration, proper spacing, authentic body balance, and natural player reactions. Every touch follows realistic momentum with accurate football physics. No clipping, collision issues, or unnatural animation. At 0:11, she reaches the edge of the penalty area. At 0:12, she unleashes a powerful long-range strike using the same football. The camera smoothly tracks the ball's entire flight from her foot to the goal in one uninterrupted motion. Realistic spin, natural speed, believable trajectory, premium broadcast-quality tracking. At 0:13, the football rockets into the top corner of the net. The goal net stretches and ripples realistically as the stadium erupts. Authentic British football commentator shouts: "What a strike! Absolutely sensational!" Crowd chants become deafening. At 0:14, she turns and sprints toward the corner flag as the camera follows closely behind. For the final second, she leaps high into the air and performs the iconic Siuuu celebration, rotating before landing confidently with feet apart and arms extended downward while facing the roaring crowd. Her half-French braid swings dramatically through the air. Brilliant stadium floodlights illuminate the scene as tens of thousands of fans celebrate around her. **Ultra-realistic cinematic football commercial, premium sports broadcast quality, seamless living-room-to-stadium transformation, one continuous unbroken shot, authentic British football commentary, realistic player movement, physically accurate football physics, strict single-ball continuity, stable first-person camera, cinematic lighting, dramatic atmosphere, crowd chants, sprinting footsteps, grass impact sounds, realistic ball contact, powerful strike, natural net deformation, HDR, ultra-detailed textures, premium realism, 15-second duration, portrait 9:16, baby-pink aesthetic, no logos, no text overlays, no subtitles, no watermark, no flickering objects, no AI artefacts, no visual glitches.

Sharon Riley

51,990 Aufrufe • vor 23 Tagen

In 2025, demand for blockchain applications with genuine real-world utility has collided with a technical barrier that leaves developers questioning what they can realistically build. Anyone building things like tokenized assets, supply chains, AI agents, or prediction markets still juggle a mess of middleware, and somehow end up spending more time stitching than innovating. How so? Every: - Bridges to move assets, - oracles to fetch data, - indexers to make that data searchable, - relayers and bots to keep everything on schedule— is necessary, but each layer also adds cost, latency, and new risks. The end result is an application that’s expensive to run, fragile under stress, and slower than the Web2 software it’s trying to replace. This is the problem Rialo says it wants to solve. Built by Subzero Labs and backed by $20 million from investors like Pantera Capital and Coinbase Ventures 🛡️, Rialo’s pitch is simple: instead of accepting the middleware tower as an unavoidable cost of doing business, compress it into the base chain itself. But Rialo doesn’t describe itself as another Layer 1, its very name, Rialo Isn’t a Layer One, makes that clear. The team frames it instead as a unified real-world network: a protocol rebuilt from the ground up with the assumption that external connectivity is not an afterthought but a core design principle. To understand what this means, consider how today’s dApps are typically assembled. A typical RWA dApp stack involves: - Oracle providers (Chainlink, Pyth, Band) for asset pricing and event settlement - Bridges (Wormhole, Multichain, custodians) for cross-chain asset movement - Indexers (The Graph, Aleph, Stacks API) for querying and preprocessing chain data - Schedulers/relayers for automated tasks and monitoring - Web2 integrations via cloud services, centralized APIs, and off-chain pipelines Each of these steps adds another vendor, another trust boundary, and another operational layer to monitor. By the time the application is live, it resembles a patchwork of loosely coupled services, each carrying its own risks. You don’t have to look far for proof: - Base went dark for 29-43 minutes in August 2025 when its sequencer misfired, freezing every DeFi app on it. - A few months earlier, an AWS outage rippled through Binance and KuCoin, stalling withdrawals because even “decentralized” systems leaned on centralized middleware. - When Infura has faltered, Ethereum dApps have gone offline in sync, not because Ethereum broke, but because the middleware holding it together did. What should feel like building an application instead feels like maintaining a fragile machine. Rialo architecture embeds the primitives that normally live in middleware directly into the protocol. Smart contracts on Rialo can: - be event-driven, able to respond not just to blockchain state changes but also to external events through built-in webhook and API triggers. - fetch data from the web natively, without relying on external oracles or relayers. - include privacy and identity management—KYC hooks and two-factor authentication, at the protocol level rather than as add-ons. - handle cross-chain communication without wrapped assets or third-party bridges. - run on a virtual machine that is compatible with ecosystems like Solana but extended with RISC-V to support modern programming concepts such as async/await and event loops. If these features work as intended, the implications are significant. Today, much of a team’s energy goes into building and maintaining infrastructure: fullnodes, indexers, monitoring scripts, oracle integrations, relayer logic, bridge infrastructure. Each requires engineering headcount and ongoing maintenance. With Rialo, much of this is absorbed by the protocol, freeing developers to concentrate on business logic. Projects can deliver production-grade dApps with smaller, leaner groups focused directly on product design and execution. Operational costs also shrink: indexing and oracle services can run into thousands of dollars a month; collapsing those into built-in functions reduces recurring expenses while simplifying onboarding for new developers. But folding middleware into the chain doesn’t erase complexity, it reshapes it. Some of the problems to be encountered include: - Scale and complexity: Rialo’s validators won’t just be securing transactions; they’ll also be securing APIs, cross-chain data, and scheduled triggers. Any failure in one subsystem could ripple across the entire network. - Performance vs. decentralization: Richer indexing, scheduling, and data ingress could make nodes heavier to run, narrowing who can realistically participate as a validator. That risks reducing the decentralization blockchains depend on for resilience. - Governance pressures: Disputes or failures involving real-world data feeds, external APIs, or cross-chain actions will arise more often, requiring not just technical fixes but robust social infrastructure, clear rules for voting, transparent arbitration, and mechanisms for community trust. Without them, Rialo risks re-centralizing decision-making around a handful of operators. Where, then, does this model make the most sense? That would be in sectors where external connectivity is indispensable and middleware bloat has consistently been a blocker: - Real-world assets: settling tokenized securities or commodities against off-chain events. - Supply chains: triggering a payment the moment a shipment clears customs, without relying on a third-party oracle. - Agent systems: AI agents interacting with real-world APIs and on-chain contracts simultaneously. - Real-time markets: prediction markets or insurance contracts that must resolve immediately against external data. For purely on-chain domains like DeFi primitives or NFTs, where composability matters more than external triggers, the advantages may be less pronounced. This shift is familiar to anyone who remembers the rise of Web2 platform services. Just as Heroku and Firebase abstracted away server maintenance so developers could focus on building products, Rialo is betting that a unified real-world network can let blockchain developers do the same. Adoption will ultimately depend on: - whether its protocol primitives mature quickly, - whether the ecosystem builds out SDKs and tooling that make them usable, - whether compliance features can adapt to changing regulations, - and whether governance proves resilient under adversarial conditions. The first applications will be the test case. If they show that Rialo can replace a fragile patchwork of middleware with a secure, auditable, and cost-effective base layer, it could set a new standard for real-world connectivity in blockchains. If not, it risks simply moving complexity from one part of the stack to another. But at a minimum, Rialo has forced the question: should real-world connectivity in blockchains continue to depend on layers of external vendors, or should it be built into the chain itself? That’s the question Rialo has put on the table — and it’s why I got interested in Rialo .
0:17

In 2025, demand for blockchain applications with genuine real-world utility has collided with a technical barrier that leaves developers questioning what they can realistically build. Anyone building things like tokenized assets, supply chains, AI agents, or prediction markets still juggle a mess of middleware, and somehow end up spending more time stitching than innovating. How so? Every: - Bridges to move assets, - oracles to fetch data, - indexers to make that data searchable, - relayers and bots to keep everything on schedule— is necessary, but each layer also adds cost, latency, and new risks. The end result is an application that’s expensive to run, fragile under stress, and slower than the Web2 software it’s trying to replace. This is the problem Rialo says it wants to solve. Built by Subzero Labs and backed by $20 million from investors like Pantera Capital and Coinbase Ventures 🛡️, Rialo’s pitch is simple: instead of accepting the middleware tower as an unavoidable cost of doing business, compress it into the base chain itself. But Rialo doesn’t describe itself as another Layer 1, its very name, Rialo Isn’t a Layer One, makes that clear. The team frames it instead as a unified real-world network: a protocol rebuilt from the ground up with the assumption that external connectivity is not an afterthought but a core design principle. To understand what this means, consider how today’s dApps are typically assembled. A typical RWA dApp stack involves: - Oracle providers (Chainlink, Pyth, Band) for asset pricing and event settlement - Bridges (Wormhole, Multichain, custodians) for cross-chain asset movement - Indexers (The Graph, Aleph, Stacks API) for querying and preprocessing chain data - Schedulers/relayers for automated tasks and monitoring - Web2 integrations via cloud services, centralized APIs, and off-chain pipelines Each of these steps adds another vendor, another trust boundary, and another operational layer to monitor. By the time the application is live, it resembles a patchwork of loosely coupled services, each carrying its own risks. You don’t have to look far for proof: - Base went dark for 29-43 minutes in August 2025 when its sequencer misfired, freezing every DeFi app on it. - A few months earlier, an AWS outage rippled through Binance and KuCoin, stalling withdrawals because even “decentralized” systems leaned on centralized middleware. - When Infura has faltered, Ethereum dApps have gone offline in sync, not because Ethereum broke, but because the middleware holding it together did. What should feel like building an application instead feels like maintaining a fragile machine. Rialo architecture embeds the primitives that normally live in middleware directly into the protocol. Smart contracts on Rialo can: - be event-driven, able to respond not just to blockchain state changes but also to external events through built-in webhook and API triggers. - fetch data from the web natively, without relying on external oracles or relayers. - include privacy and identity management—KYC hooks and two-factor authentication, at the protocol level rather than as add-ons. - handle cross-chain communication without wrapped assets or third-party bridges. - run on a virtual machine that is compatible with ecosystems like Solana but extended with RISC-V to support modern programming concepts such as async/await and event loops. If these features work as intended, the implications are significant. Today, much of a team’s energy goes into building and maintaining infrastructure: fullnodes, indexers, monitoring scripts, oracle integrations, relayer logic, bridge infrastructure. Each requires engineering headcount and ongoing maintenance. With Rialo, much of this is absorbed by the protocol, freeing developers to concentrate on business logic. Projects can deliver production-grade dApps with smaller, leaner groups focused directly on product design and execution. Operational costs also shrink: indexing and oracle services can run into thousands of dollars a month; collapsing those into built-in functions reduces recurring expenses while simplifying onboarding for new developers. But folding middleware into the chain doesn’t erase complexity, it reshapes it. Some of the problems to be encountered include: - Scale and complexity: Rialo’s validators won’t just be securing transactions; they’ll also be securing APIs, cross-chain data, and scheduled triggers. Any failure in one subsystem could ripple across the entire network. - Performance vs. decentralization: Richer indexing, scheduling, and data ingress could make nodes heavier to run, narrowing who can realistically participate as a validator. That risks reducing the decentralization blockchains depend on for resilience. - Governance pressures: Disputes or failures involving real-world data feeds, external APIs, or cross-chain actions will arise more often, requiring not just technical fixes but robust social infrastructure, clear rules for voting, transparent arbitration, and mechanisms for community trust. Without them, Rialo risks re-centralizing decision-making around a handful of operators. Where, then, does this model make the most sense? That would be in sectors where external connectivity is indispensable and middleware bloat has consistently been a blocker: - Real-world assets: settling tokenized securities or commodities against off-chain events. - Supply chains: triggering a payment the moment a shipment clears customs, without relying on a third-party oracle. - Agent systems: AI agents interacting with real-world APIs and on-chain contracts simultaneously. - Real-time markets: prediction markets or insurance contracts that must resolve immediately against external data. For purely on-chain domains like DeFi primitives or NFTs, where composability matters more than external triggers, the advantages may be less pronounced. This shift is familiar to anyone who remembers the rise of Web2 platform services. Just as Heroku and Firebase abstracted away server maintenance so developers could focus on building products, Rialo is betting that a unified real-world network can let blockchain developers do the same. Adoption will ultimately depend on: - whether its protocol primitives mature quickly, - whether the ecosystem builds out SDKs and tooling that make them usable, - whether compliance features can adapt to changing regulations, - and whether governance proves resilient under adversarial conditions. The first applications will be the test case. If they show that Rialo can replace a fragile patchwork of middleware with a secure, auditable, and cost-effective base layer, it could set a new standard for real-world connectivity in blockchains. If not, it risks simply moving complexity from one part of the stack to another. But at a minimum, Rialo has forced the question: should real-world connectivity in blockchains continue to depend on layers of external vendors, or should it be built into the chain itself? That’s the question Rialo has put on the table — and it’s why I got interested in Rialo .

Jen

12,178 Aufrufe • vor 10 Monaten

Made with seedance 2.0 + GPT Image 2 on Yapper Prompt. Image mage1 fights three opponents inside a Japanese high school classroom in an intense, highly dynamic one-take action sequence. The classroom is filled with wooden desks, chairs, school bags, a chalkboard, sliding windows, curtains, fluorescent ceiling lights, posters, books, and scattered papers. The fight is fast, physical, and highly interactive with the environment. The woman moves between the desks with sharp agility, dodging attacks from all three opponents at once. She vaults over desks, slides across tabletops, kicks chairs into attackers, blocks strikes using classroom objects, grabs a backpack to deflect a hit, and uses the narrow aisles between desks to redirect momentum. Papers fly through the air, chairs scrape across the floor, desks topple, curtains whip from the movement, and sunlight cuts through the windows, catching dust particles in the air. The camera is extremely dynamic and close to the action, never a static wide shot. Use a fast-moving one-take camera that constantly follows, circles, ducks, whips, and pushes through the fight. The camera moves between desks, swings around the woman as she turns, rushes backward as opponents charge, drops low near the floor during leg sweeps, rises suddenly as she jumps over a desk, and whips around quickly to reveal the next attacker. The framing should feel urgent, handheld, immersive, and physically present inside the classroom. The action should feature strong choreography, realistic body movement, believable impact, fast reaction timing, close-range combat, and continuous motion. Make the scene feel like a high-budget martial arts action sequence captured in one uninterrupted shot. Use natural classroom lighting mixed with warm afternoon sunlight through the windows, realistic shadows, practical motion blur, grounded textures, real-world imperfections, and a raw cinematic look. No glossy AI finish, no overly polished CGI, and no static long-shot framing. Negative prompts: static camera, slow movement, shaky low-quality footage, blurry subject, distorted body, unrealistic swinging physics, cartoon style, flat lighting, dull colors, overexposed sky, broken buildings, empty streets, low detail, awkward camera cuts, poor motion continuity, AI glossy look, overly polished CGI, plastic-looking skin, waxy skin texture, hyper-smooth surfaces, artificial shine, fake cinematic bloom, excessive lens flare, unrealistic HDR, oversaturated colors, neon color grading, game-engine look, Unreal Engine render look, synthetic lighting, studio lighting, perfect clean reflections, overly sharp digital image, crispy AI detail, overprocessed image, fake depth of field, exaggerated bokeh, unnatural contrast, overly smooth motion, floating physics, rubbery body movement, distorted anatomy, warped limbs, inconsistent body proportions, blurry face, melted facial features, duplicated limbs, broken hands, unnatural pose, stiff action, low-quality motion interpolation, smeared motion blur, ghosting, frame blending artifacts, unstable subject tracking, camera jitter without purpose, awkward cuts, poor continuity, artificial city layout, empty streets, repeated cars, duplicated buildings, warped skyscrapers, fake traffic, low-detail background, superhero suit, comic-book look, stylized animation, overly dramatic VFX, unrealistic shadows, fake sun rays, unnatural haze, overexposed highlights, crushed blacks, sterile clean environments, no atmosphere, no real-world imperfections.
0:15

Made with seedance 2.0 + GPT Image 2 on Yapper Prompt. Image mage1 fights three opponents inside a Japanese high school classroom in an intense, highly dynamic one-take action sequence. The classroom is filled with wooden desks, chairs, school bags, a chalkboard, sliding windows, curtains, fluorescent ceiling lights, posters, books, and scattered papers. The fight is fast, physical, and highly interactive with the environment. The woman moves between the desks with sharp agility, dodging attacks from all three opponents at once. She vaults over desks, slides across tabletops, kicks chairs into attackers, blocks strikes using classroom objects, grabs a backpack to deflect a hit, and uses the narrow aisles between desks to redirect momentum. Papers fly through the air, chairs scrape across the floor, desks topple, curtains whip from the movement, and sunlight cuts through the windows, catching dust particles in the air. The camera is extremely dynamic and close to the action, never a static wide shot. Use a fast-moving one-take camera that constantly follows, circles, ducks, whips, and pushes through the fight. The camera moves between desks, swings around the woman as she turns, rushes backward as opponents charge, drops low near the floor during leg sweeps, rises suddenly as she jumps over a desk, and whips around quickly to reveal the next attacker. The framing should feel urgent, handheld, immersive, and physically present inside the classroom. The action should feature strong choreography, realistic body movement, believable impact, fast reaction timing, close-range combat, and continuous motion. Make the scene feel like a high-budget martial arts action sequence captured in one uninterrupted shot. Use natural classroom lighting mixed with warm afternoon sunlight through the windows, realistic shadows, practical motion blur, grounded textures, real-world imperfections, and a raw cinematic look. No glossy AI finish, no overly polished CGI, and no static long-shot framing. Negative prompts: static camera, slow movement, shaky low-quality footage, blurry subject, distorted body, unrealistic swinging physics, cartoon style, flat lighting, dull colors, overexposed sky, broken buildings, empty streets, low detail, awkward camera cuts, poor motion continuity, AI glossy look, overly polished CGI, plastic-looking skin, waxy skin texture, hyper-smooth surfaces, artificial shine, fake cinematic bloom, excessive lens flare, unrealistic HDR, oversaturated colors, neon color grading, game-engine look, Unreal Engine render look, synthetic lighting, studio lighting, perfect clean reflections, overly sharp digital image, crispy AI detail, overprocessed image, fake depth of field, exaggerated bokeh, unnatural contrast, overly smooth motion, floating physics, rubbery body movement, distorted anatomy, warped limbs, inconsistent body proportions, blurry face, melted facial features, duplicated limbs, broken hands, unnatural pose, stiff action, low-quality motion interpolation, smeared motion blur, ghosting, frame blending artifacts, unstable subject tracking, camera jitter without purpose, awkward cuts, poor continuity, artificial city layout, empty streets, repeated cars, duplicated buildings, warped skyscrapers, fake traffic, low-detail background, superhero suit, comic-book look, stylized animation, overly dramatic VFX, unrealistic shadows, fake sun rays, unnatural haze, overexposed highlights, crushed blacks, sterile clean environments, no atmosphere, no real-world imperfections.

auqib

20,011 Aufrufe • vor 2 Monaten

Seedance 2.0 on FlovaAI =================== Prompt: [Reference Identity Lock] Image 1 is ONLY the main female protagonist. Her face, hairstyle, body type, and outfit must match Image 1 exactly and stay consistent for the entire video. Image 2 is ONLY a uniform reference. All four opponents wear the school uniform shown in Image 2. Never swap, merge, duplicate, or blend identities. The protagonist's identity comes ONLY from Image 1. The four opponents have NO reference images. They are defined by the text descriptions below. The four opponents must not resemble the protagonist, and they must not resemble each other. All five characters must remain clearly distinct and recognizable until the end. [Priority Order] 1. Preserve the protagonist's identity from Image 1. 2. Keep the four opponents visually distinct from her and from each other. 3. Maintain one continuous shot with no cuts. 4. Keep the classroom layout spatially consistent. 5. Make the action fast but readable and physically connected. 6. Keep the tone as a Korean school action drama, stylish but grounded. Korean school action drama classroom fight scene — 15 seconds, ONE CONTINUOUS SHOT, NO CUTS. A single uninterrupted handheld shot. No cuts, no scene transitions, no montage. The camera should feel handheld, with micro-jitters, slight rolling shutter, and raw unstable realism. The camera must physically travel through the same classroom space. Every transition must be motivated by camera movement, not editing. Whip pans are allowed, but they must not hide a cut. Do not teleport the camera or characters. The classroom layout and character positions must remain spatially consistent. Audio: No music. Only realistic school and classroom ambient sounds: old fluorescent light hum, distant hallway noise, ceiling fan, shoes scraping the floor, desks dragging, chair legs screeching, cloth friction, dull body impacts, and breathing that gradually becomes heavier. Breathing continues throughout the scene and keeps building. Lighting: Late afternoon in a Korean high school classroom. Mixed cool fluorescent light and warm sunlight through the windows. Dust floating in the sunlight. Soft fan shadows moving across desks and school uniforms. Main character: The Korean female high school student from Image 1, age 17–18. Cold, emotionless, calm, and intimidating. She barely speaks and does not scream during the fight. She remains composed from beginning to end. Her movements are efficient, explosive, and precise. Even if her frame is not large, she dominates through speed, timing, and accuracy. Main outfit: Exactly the outfit shown in Image 1. Do not change its colors, design, or details. Her jacket or outer layer is either removed and hanging on a chair, or worn in a slightly messy way. The action must be non-sexualized and combat-focused. Fabric movement, dust, sweat, wrinkles, and impact response should feel realistic. Opponent rules: Four Korean female high school students, all wearing the Hanlim Multi Art School uniform shown in Image 2. They have no reference images. Define them strictly by these descriptions and keep each one consistent: Opponent A: short black bob with straight bangs, medium build, round face. Opponent B: long straight hair tied in a high ponytail, tall and lean, sharp jawline. Opponent C: shoulder-length hair with side-swept bangs, slim build, narrow face. Opponent D: long wavy hair worn loose, slightly stocky and broad-shouldered. A, B, C, and D must each keep clearly different faces, hairstyles, body shapes, and silhouettes. They must not resemble the protagonist, and they must not resemble each other. No face duplication, no face merging, no identity confusion. Environment: An empty classroom at Hanlim Multi Art School, a Korean performing arts high school in Seoul. Green chalkboard, chalk tray, worn wooden desks, plastic chairs, classroom clock, class schedule poster, discipline/life-guidance posters, cleaning tools, blinds or curtains, wall study materials, and a slightly scuffed floor. Desks and chairs should react naturally to impacts, sliding, shaking, and collapsing when hit. Camera framing rules: Even during kicks, framing should stay around chest-level or eye-level. No low-angle shots under the skirt. Do not focus on legs, thighs, underwear, or fetish-like details. All action framing must prioritize faces, upper-body motion, impact, and spatial choreography. Continuous action and camera choreography: From 0 to 15 seconds, the fight continues without any cuts. The action should be stylish but readable, and every movement must be physically connected. 0–3s: The camera starts behind the protagonist at a slightly low handheld angle, drifting left through the classroom aisle. Opponent A grabs the protagonist's shoulder roughly and says in Korean: "야, 너 지금 뭐 하자는 거야?" The protagonist silently turns and lands one hard straight punch to A's face. At impact, use a very brief 15% slow motion: cheek ripple, dust particles, deep thud. A falls sideways into a desk. The camera dips slightly from the shock, then whip-pans right without cutting. 3–6s: Opponent B charges in from the right. The protagonist steps forward instead of retreating. A short body shot to the stomach. Immediate uppercut to the chin. Without pausing, she drives forward into a flying knee to B's chest. B is thrown backward across or into a desk. The camera follows the forward motion low, then rebounds upward with the impact. 6–9s: Opponent D attacks with two fast punches. The protagonist deflects both strikes with her arms, then flows into a turning backfist to D's face. As D staggers, she continues the same rotation into a spinning back elbow that lands hard on D's jaw or temple. D crashes sideways into two or three desks. The camera arcs around her shoulder and jitters slightly at each impact. No cuts. 9–12s: Opponent C rushes in from the chalkboard side. The protagonist clearly grabs C's collar with her left hand. C's face must be fully visible from the front and clearly different from the protagonist. The protagonist lands one short, hard punch to C's face, then immediately throws a powerful high kick or flying high kick into C's chest. The force sends C backward into the green chalkboard. The protagonist remains in the foreground and never touches the board. The protagonist's face should be side-profile or partially obscured. C's face should be clearly visible from the front at the moment of impact. Their faces must never overlap in frame. Use a very brief 20% slow motion at the chalkboard impact: chalk dust bursts outward, and C slides down the board. The camera pushes up with the impact, then tilts down as C slides. 12–15s: Through the chalk dust, the camera hard-pans right. D makes one final charge. The protagonist sidesteps and lands a tight uppercut to D's chin, followed immediately by a cross. D crashes into a row of desks, causing a chain reaction of collapsing desks and chairs. The camera drifts forward slowly. The protagonist adjusts her loose tie or ribbon and brushes chalk dust off her shoulder. Her expression stays cold and serious. She walks past the camera and exits the frame. Dust floats in the sunlight. Natural ending. =================== Made with Flova #FlovaAI #FlovaCPP
0:15

Seedance 2.0 on FlovaAI =================== Prompt: [Reference Identity Lock] Image 1 is ONLY the main female protagonist. Her face, hairstyle, body type, and outfit must match Image 1 exactly and stay consistent for the entire video. Image 2 is ONLY a uniform reference. All four opponents wear the school uniform shown in Image 2. Never swap, merge, duplicate, or blend identities. The protagonist's identity comes ONLY from Image 1. The four opponents have NO reference images. They are defined by the text descriptions below. The four opponents must not resemble the protagonist, and they must not resemble each other. All five characters must remain clearly distinct and recognizable until the end. [Priority Order] 1. Preserve the protagonist's identity from Image 1. 2. Keep the four opponents visually distinct from her and from each other. 3. Maintain one continuous shot with no cuts. 4. Keep the classroom layout spatially consistent. 5. Make the action fast but readable and physically connected. 6. Keep the tone as a Korean school action drama, stylish but grounded. Korean school action drama classroom fight scene — 15 seconds, ONE CONTINUOUS SHOT, NO CUTS. A single uninterrupted handheld shot. No cuts, no scene transitions, no montage. The camera should feel handheld, with micro-jitters, slight rolling shutter, and raw unstable realism. The camera must physically travel through the same classroom space. Every transition must be motivated by camera movement, not editing. Whip pans are allowed, but they must not hide a cut. Do not teleport the camera or characters. The classroom layout and character positions must remain spatially consistent. Audio: No music. Only realistic school and classroom ambient sounds: old fluorescent light hum, distant hallway noise, ceiling fan, shoes scraping the floor, desks dragging, chair legs screeching, cloth friction, dull body impacts, and breathing that gradually becomes heavier. Breathing continues throughout the scene and keeps building. Lighting: Late afternoon in a Korean high school classroom. Mixed cool fluorescent light and warm sunlight through the windows. Dust floating in the sunlight. Soft fan shadows moving across desks and school uniforms. Main character: The Korean female high school student from Image 1, age 17–18. Cold, emotionless, calm, and intimidating. She barely speaks and does not scream during the fight. She remains composed from beginning to end. Her movements are efficient, explosive, and precise. Even if her frame is not large, she dominates through speed, timing, and accuracy. Main outfit: Exactly the outfit shown in Image 1. Do not change its colors, design, or details. Her jacket or outer layer is either removed and hanging on a chair, or worn in a slightly messy way. The action must be non-sexualized and combat-focused. Fabric movement, dust, sweat, wrinkles, and impact response should feel realistic. Opponent rules: Four Korean female high school students, all wearing the Hanlim Multi Art School uniform shown in Image 2. They have no reference images. Define them strictly by these descriptions and keep each one consistent: Opponent A: short black bob with straight bangs, medium build, round face. Opponent B: long straight hair tied in a high ponytail, tall and lean, sharp jawline. Opponent C: shoulder-length hair with side-swept bangs, slim build, narrow face. Opponent D: long wavy hair worn loose, slightly stocky and broad-shouldered. A, B, C, and D must each keep clearly different faces, hairstyles, body shapes, and silhouettes. They must not resemble the protagonist, and they must not resemble each other. No face duplication, no face merging, no identity confusion. Environment: An empty classroom at Hanlim Multi Art School, a Korean performing arts high school in Seoul. Green chalkboard, chalk tray, worn wooden desks, plastic chairs, classroom clock, class schedule poster, discipline/life-guidance posters, cleaning tools, blinds or curtains, wall study materials, and a slightly scuffed floor. Desks and chairs should react naturally to impacts, sliding, shaking, and collapsing when hit. Camera framing rules: Even during kicks, framing should stay around chest-level or eye-level. No low-angle shots under the skirt. Do not focus on legs, thighs, underwear, or fetish-like details. All action framing must prioritize faces, upper-body motion, impact, and spatial choreography. Continuous action and camera choreography: From 0 to 15 seconds, the fight continues without any cuts. The action should be stylish but readable, and every movement must be physically connected. 0–3s: The camera starts behind the protagonist at a slightly low handheld angle, drifting left through the classroom aisle. Opponent A grabs the protagonist's shoulder roughly and says in Korean: "야, 너 지금 뭐 하자는 거야?" The protagonist silently turns and lands one hard straight punch to A's face. At impact, use a very brief 15% slow motion: cheek ripple, dust particles, deep thud. A falls sideways into a desk. The camera dips slightly from the shock, then whip-pans right without cutting. 3–6s: Opponent B charges in from the right. The protagonist steps forward instead of retreating. A short body shot to the stomach. Immediate uppercut to the chin. Without pausing, she drives forward into a flying knee to B's chest. B is thrown backward across or into a desk. The camera follows the forward motion low, then rebounds upward with the impact. 6–9s: Opponent D attacks with two fast punches. The protagonist deflects both strikes with her arms, then flows into a turning backfist to D's face. As D staggers, she continues the same rotation into a spinning back elbow that lands hard on D's jaw or temple. D crashes sideways into two or three desks. The camera arcs around her shoulder and jitters slightly at each impact. No cuts. 9–12s: Opponent C rushes in from the chalkboard side. The protagonist clearly grabs C's collar with her left hand. C's face must be fully visible from the front and clearly different from the protagonist. The protagonist lands one short, hard punch to C's face, then immediately throws a powerful high kick or flying high kick into C's chest. The force sends C backward into the green chalkboard. The protagonist remains in the foreground and never touches the board. The protagonist's face should be side-profile or partially obscured. C's face should be clearly visible from the front at the moment of impact. Their faces must never overlap in frame. Use a very brief 20% slow motion at the chalkboard impact: chalk dust bursts outward, and C slides down the board. The camera pushes up with the impact, then tilts down as C slides. 12–15s: Through the chalk dust, the camera hard-pans right. D makes one final charge. The protagonist sidesteps and lands a tight uppercut to D's chin, followed immediately by a cross. D crashes into a row of desks, causing a chain reaction of collapsing desks and chairs. The camera drifts forward slowly. The protagonist adjusts her loose tie or ribbon and brushes chalk dust off her shoulder. Her expression stays cold and serious. She walks past the camera and exits the frame. Dust floats in the sunlight. Natural ending. =================== Made with Flova #FlovaAI #FlovaCPP

TSUBAKI

18,464 Aufrufe • vor 23 Tagen

Would you jump off the world's tallest building on purpose? Made by using Nano Banana pro + Seedance 2.0 on BudgetPixel AI image1 is the main character throughout the entire video. Use the exact face, hair, clothing, skin tone, necklace, and sneakers from image1. Identity lock enabled. Face, hair, clothing, and body proportions remain identical in every frame. A young Western woman with wavy blonde hair sits on the outer corner ledge of an observation deck high on the Burj Khalifa in Downtown Dubai, facing toward the deck. Her legs hang over the building edge while she leans her upper body backward into open air beyond the building. Handheld cinematic camera from slightly behind and beside her. Golden hour sunlight reflects across surrounding skyscrapers. She smiles naturally and says, "Damn, yesterday was crazy." She relaxes, stretches her shoulders and arms, then says, "and today we go again." She grins, looks toward the skyline, intentionally pushes herself backward off the ledge, committing to the jump. Her body rotates naturally as gravity pulls her downward along the side of the skyscraper. Physically accurate freefall with realistic acceleration, wind resistance, body weight, and momentum. Handheld follow camera dives after her with subtle natural shake. Just before reaching street level, she confidently fires a handheld grappling-rope launcher toward a nearby building. The steel hook anchors realistically to the structure. Tension builds and converts the fall into a smooth pendulum swing. She then chains three full pendulum swings in a row, one after another, releasing the line at the peak of each arc and immediately re-firing the rope. First swing arcs low between two towers, second swing whips around a building corner, third swing launches her high over Sheikh Zayed Road. Each release-and-refire is fast and continuous — swift agile maneuvering between skyscrapers with sharp directional changes and no pause between swings. The camera follows from dynamic handheld tracking angles, over-the-shoulder views, and wide aerial reveals. She swings freely, laughing and enjoying the ride. Her wavy blonde hair whips naturally in the wind. A flock of birds passes nearby. An aeroplane crosses the skyline at realistic distance and speed. Wind interacts naturally with her hoodie and hair. Final sweeping handheld shot above Dubai at sunset as she continues swinging forward and shouts loudly, "WOOHUUUU!" Audio: rooftop wind, distant city ambience, rushing air during freefall, mechanical grappling-launch sound, rope tension snap, birds flapping, aeroplane, joyful laughter, Dubai traffic far below. 4K Ultra HD, rich details, sharp clarity, cinematic texture, stable picture. Maintain face, hair, and clothing consistency, no distortion, high detail. Generate video without subtitles. Aspect Ratio: 16:9 Duration: 15s Camera: Non-fixed (Handheld) image1 = Character identity reference
0:14

Would you jump off the world's tallest building on purpose? Made by using Nano Banana pro + Seedance 2.0 on BudgetPixel AI image1 is the main character throughout the entire video. Use the exact face, hair, clothing, skin tone, necklace, and sneakers from image1. Identity lock enabled. Face, hair, clothing, and body proportions remain identical in every frame. A young Western woman with wavy blonde hair sits on the outer corner ledge of an observation deck high on the Burj Khalifa in Downtown Dubai, facing toward the deck. Her legs hang over the building edge while she leans her upper body backward into open air beyond the building. Handheld cinematic camera from slightly behind and beside her. Golden hour sunlight reflects across surrounding skyscrapers. She smiles naturally and says, "Damn, yesterday was crazy." She relaxes, stretches her shoulders and arms, then says, "and today we go again." She grins, looks toward the skyline, intentionally pushes herself backward off the ledge, committing to the jump. Her body rotates naturally as gravity pulls her downward along the side of the skyscraper. Physically accurate freefall with realistic acceleration, wind resistance, body weight, and momentum. Handheld follow camera dives after her with subtle natural shake. Just before reaching street level, she confidently fires a handheld grappling-rope launcher toward a nearby building. The steel hook anchors realistically to the structure. Tension builds and converts the fall into a smooth pendulum swing. She then chains three full pendulum swings in a row, one after another, releasing the line at the peak of each arc and immediately re-firing the rope. First swing arcs low between two towers, second swing whips around a building corner, third swing launches her high over Sheikh Zayed Road. Each release-and-refire is fast and continuous — swift agile maneuvering between skyscrapers with sharp directional changes and no pause between swings. The camera follows from dynamic handheld tracking angles, over-the-shoulder views, and wide aerial reveals. She swings freely, laughing and enjoying the ride. Her wavy blonde hair whips naturally in the wind. A flock of birds passes nearby. An aeroplane crosses the skyline at realistic distance and speed. Wind interacts naturally with her hoodie and hair. Final sweeping handheld shot above Dubai at sunset as she continues swinging forward and shouts loudly, "WOOHUUUU!" Audio: rooftop wind, distant city ambience, rushing air during freefall, mechanical grappling-launch sound, rope tension snap, birds flapping, aeroplane, joyful laughter, Dubai traffic far below. 4K Ultra HD, rich details, sharp clarity, cinematic texture, stable picture. Maintain face, hair, and clothing consistency, no distortion, high detail. Generate video without subtitles. Aspect Ratio: 16:9 Duration: 15s Camera: Non-fixed (Handheld) image1 = Character identity reference

Sharon Riley

38,042 Aufrufe • vor 1 Monat