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Improved Wan Animate - SteadyDancer: transforms static photos into coherent videos while strictly preventing identity drift; SOTA motion control & fidelity with significantly reduced training resources.

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Wildminder

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23,768 görüntüleme • 9 ay önce •via X (Twitter)

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If you can explain it, you can animate it. Turn your ideas into custom motion graphics in minutes with HeyGen’s newest feature, Motion Designer. Keep every animation on brand with your own style and colors. No templates. No After Effects. No endless tutorials. Create professional, controllable motion graphics for explainers, training videos, and product launches, all inside HeyGen. Every scene, every transition, and every detail designed to help your ideas move fast. Because great communication deserves more than static slides. It deserves motion. Don’t just tell your story. Free it.
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If you can explain it, you can animate it. Turn your ideas into custom motion graphics in minutes with HeyGen’s newest feature, Motion Designer. Keep every animation on brand with your own style and colors. No templates. No After Effects. No endless tutorials. Create professional, controllable motion graphics for explainers, training videos, and product launches, all inside HeyGen. Every scene, every transition, and every detail designed to help your ideas move fast. Because great communication deserves more than static slides. It deserves motion. Don’t just tell your story. Free it.

HeyGen

1,017,849 görüntüleme • 10 ay önce

4DGT: Learning a 4D Gaussian Transformer Using Real-World Monocular Videos Abstract: We propose 4DGT, a 4D Gaussian-based Transformer model for dynamic scene reconstruction, trained entirely on real-world monocular posed videos. Using 4D Gaussian as an inductive bias, 4DGT unifies static and dynamic components, enabling the modeling of complex, time-varying environments with varying object lifespans. We introduced a novel density control strategy in training, which allows our 4DGT to handle longer space-time input while maintaining efficient rendering at runtime. Our model processes 64 consecutive posed frames in a rolling-window fashion, predicting consistent 4D Gaussians in the scene. Unlike optimization-based methods, 4DGT performs purely feed-forward inference, reducing reconstruction time from hours to seconds and scaling effectively to long video sequences. Trained only on large-scale monocular posed video datasets, 4DGT can significantly outperform prior Gaussian-based networks in real-world videos and achieve on-par accuracy with optimization-based methods on cross-domain videos.
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4DGT: Learning a 4D Gaussian Transformer Using Real-World Monocular Videos Abstract: We propose 4DGT, a 4D Gaussian-based Transformer model for dynamic scene reconstruction, trained entirely on real-world monocular posed videos. Using 4D Gaussian as an inductive bias, 4DGT unifies static and dynamic components, enabling the modeling of complex, time-varying environments with varying object lifespans. We introduced a novel density control strategy in training, which allows our 4DGT to handle longer space-time input while maintaining efficient rendering at runtime. Our model processes 64 consecutive posed frames in a rolling-window fashion, predicting consistent 4D Gaussians in the scene. Unlike optimization-based methods, 4DGT performs purely feed-forward inference, reducing reconstruction time from hours to seconds and scaling effectively to long video sequences. Trained only on large-scale monocular posed video datasets, 4DGT can significantly outperform prior Gaussian-based networks in real-world videos and achieve on-par accuracy with optimization-based methods on cross-domain videos.

MrNeRF

34,782 görüntüleme • 1 yıl önce

🎉Introducing Sonic: Shifting Focus to Global Audio Perception in Portrait Animation 🎶 👉 What's New? 1️⃣ Breathe life into static images! Single image + any audio → speeches, singing, & beyond! 2️⃣ Temporal Audio Learning harnesses global audio context for precise lip-sync & natural expressions 3️⃣ Decoupled Motion Control disentangles head/expression movements for vivid adaptability 4️⃣ Time-aware Fusion ensures seamless video continuity across frames 👉 Why It Matters? ✅ First method to balance audio precision, motion diversity, & temporal stability ✅ Empowers animators to create long-form videos (e.g., ads/vlogs) with 1-click workflows 🖱 ✅ Industry-level quality: Enhanced head poses, authentic emotions, & identity consistency 👉 Game-changing for: ▶️ Advertising & media 🎥 | ▶️ Virtual influencers 📱 ▶️Interactive entertainment 🎮 | ▶️AI-driven education 🎓 📚 Explore Sonic’s Magic: 🔗 Project: 🔗 Code: 🔗 Paper:
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🎉Introducing Sonic: Shifting Focus to Global Audio Perception in Portrait Animation 🎶 👉 What's New? 1️⃣ Breathe life into static images! Single image + any audio → speeches, singing, & beyond! 2️⃣ Temporal Audio Learning harnesses global audio context for precise lip-sync & natural expressions 3️⃣ Decoupled Motion Control disentangles head/expression movements for vivid adaptability 4️⃣ Time-aware Fusion ensures seamless video continuity across frames 👉 Why It Matters? ✅ First method to balance audio precision, motion diversity, & temporal stability ✅ Empowers animators to create long-form videos (e.g., ads/vlogs) with 1-click workflows 🖱 ✅ Industry-level quality: Enhanced head poses, authentic emotions, & identity consistency 👉 Game-changing for: ▶️ Advertising & media 🎥 | ▶️ Virtual influencers 📱 ▶️Interactive entertainment 🎮 | ▶️AI-driven education 🎓 📚 Explore Sonic’s Magic: 🔗 Project: 🔗 Code: 🔗 Paper:

Tencent HY

39,079 görüntüleme • 1 yıl önce

Fast View Synthesis of Casual Videos paper page: Novel view synthesis from an in-the-wild video is difficult due to challenges like scene dynamics and lack of parallax. While existing methods have shown promising results with implicit neural radiance fields, they are slow to train and render. This paper revisits explicit video representations to synthesize high-quality novel views from a monocular video efficiently. We treat static and dynamic video content separately. Specifically, we build a global static scene model using an extended plane-based scene representation to synthesize temporally coherent novel video. Our plane-based scene representation is augmented with spherical harmonics and displacement maps to capture view-dependent effects and model non-planar complex surface geometry. We opt to represent the dynamic content as per-frame point clouds for efficiency. While such representations are inconsistency-prone, minor temporal inconsistencies are perceptually masked due to motion. We develop a method to quickly estimate such a hybrid video representation and render novel views in real time. Our experiments show that our method can render high-quality novel views from an in-the-wild video with comparable quality to state-of-the-art methods while being 100x faster in training and enabling real-time rendering.
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Fast View Synthesis of Casual Videos paper page: Novel view synthesis from an in-the-wild video is difficult due to challenges like scene dynamics and lack of parallax. While existing methods have shown promising results with implicit neural radiance fields, they are slow to train and render. This paper revisits explicit video representations to synthesize high-quality novel views from a monocular video efficiently. We treat static and dynamic video content separately. Specifically, we build a global static scene model using an extended plane-based scene representation to synthesize temporally coherent novel video. Our plane-based scene representation is augmented with spherical harmonics and displacement maps to capture view-dependent effects and model non-planar complex surface geometry. We opt to represent the dynamic content as per-frame point clouds for efficiency. While such representations are inconsistency-prone, minor temporal inconsistencies are perceptually masked due to motion. We develop a method to quickly estimate such a hybrid video representation and render novel views in real time. Our experiments show that our method can render high-quality novel views from an in-the-wild video with comparable quality to state-of-the-art methods while being 100x faster in training and enabling real-time rendering.

AK

20,668 görüntüleme • 2 yıl önce

Alibaba presents MIMO Controllable Character Video Synthesis with Spatial Decomposed Modeling Character video synthesis aims to produce realistic videos of animatable characters within lifelike scenes. As a fundamental problem in the computer vision and graphics community, 3D works typically require multi-view captures for per-case training, which severely limits their applicability of modeling arbitrary characters in a short time. Recent 2D methods break this limitation via pre-trained diffusion models, but they struggle for pose generality and scene interaction. To this end, we propose MIMO, a novel framework which can not only synthesize character videos with controllable attributes (i.e., character, motion and scene) provided by simple user inputs, but also simultaneously achieve advanced scalability to arbitrary characters, generality to novel 3D motions, and applicability to interactive real-world scenes in a unified framework. The core idea is to encode the 2D video to compact spatial codes, considering the inherent 3D nature of video occurrence. Concretely, we lift the 2D frame pixels into 3D using monocular depth estimators, and decompose the video clip to three spatial components (i.e., main human, underlying scene, and floating occlusion) in hierarchical layers based on the 3D depth. These components are further encoded to canonical identity code, structured motion code and full scene code, which are utilized as control signals of synthesis process. The design of spatial decomposed modeling enables flexible user control, complex motion expression, as well as 3D-aware synthesis for scene interactions. Experimental results demonstrate effectiveness and robustness of the proposed method.
1:29

Alibaba presents MIMO Controllable Character Video Synthesis with Spatial Decomposed Modeling Character video synthesis aims to produce realistic videos of animatable characters within lifelike scenes. As a fundamental problem in the computer vision and graphics community, 3D works typically require multi-view captures for per-case training, which severely limits their applicability of modeling arbitrary characters in a short time. Recent 2D methods break this limitation via pre-trained diffusion models, but they struggle for pose generality and scene interaction. To this end, we propose MIMO, a novel framework which can not only synthesize character videos with controllable attributes (i.e., character, motion and scene) provided by simple user inputs, but also simultaneously achieve advanced scalability to arbitrary characters, generality to novel 3D motions, and applicability to interactive real-world scenes in a unified framework. The core idea is to encode the 2D video to compact spatial codes, considering the inherent 3D nature of video occurrence. Concretely, we lift the 2D frame pixels into 3D using monocular depth estimators, and decompose the video clip to three spatial components (i.e., main human, underlying scene, and floating occlusion) in hierarchical layers based on the 3D depth. These components are further encoded to canonical identity code, structured motion code and full scene code, which are utilized as control signals of synthesis process. The design of spatial decomposed modeling enables flexible user control, complex motion expression, as well as 3D-aware synthesis for scene interactions. Experimental results demonstrate effectiveness and robustness of the proposed method.

AK

149,079 görüntüleme • 1 yıl önce

Google presents Still-Moving Customized Video Generation without Customized Video Data Customizing text-to-image (T2I) models has seen tremendous progress recently, particularly in areas such as personalization, stylization, and conditional generation. However, expanding this progress to video generation is still in its infancy, primarily due to the lack of customized video data. In this work, we introduce Still-Moving, a novel generic framework for customizing a text-to-video (T2V) model, without requiring any customized video data. The framework applies to the prominent T2V design where the video model is built over a text-to-image (T2I) model (e.g., via inflation). We assume access to a customized version of the T2I model, trained only on still image data (e.g., using DreamBooth or StyleDrop). Naively plugging in the weights of the customized T2I model into the T2V model often leads to significant artifacts or insufficient adherence to the customization data. To overcome this issue, we train lightweight Spatial Adapters that adjust the features produced by the injected T2I layers. Importantly, our adapters are trained on "frozen videos" (i.e., repeated images), constructed from image samples generated by the customized T2I model. This training is facilitated by a novel Motion Adapter module, which allows us to train on such static videos while preserving the motion prior of the video model. At test time, we remove the Motion Adapter modules and leave in only the trained Spatial Adapters. This restores the motion prior of the T2V model while adhering to the spatial prior of the customized T2I model. We demonstrate the effectiveness of our approach on diverse tasks including personalized, stylized, and conditional generation. In all evaluated scenarios, our method seamlessly integrates the spatial prior of the customized T2I model with a motion prior supplied by the T2V model.
1:43

Google presents Still-Moving Customized Video Generation without Customized Video Data Customizing text-to-image (T2I) models has seen tremendous progress recently, particularly in areas such as personalization, stylization, and conditional generation. However, expanding this progress to video generation is still in its infancy, primarily due to the lack of customized video data. In this work, we introduce Still-Moving, a novel generic framework for customizing a text-to-video (T2V) model, without requiring any customized video data. The framework applies to the prominent T2V design where the video model is built over a text-to-image (T2I) model (e.g., via inflation). We assume access to a customized version of the T2I model, trained only on still image data (e.g., using DreamBooth or StyleDrop). Naively plugging in the weights of the customized T2I model into the T2V model often leads to significant artifacts or insufficient adherence to the customization data. To overcome this issue, we train lightweight Spatial Adapters that adjust the features produced by the injected T2I layers. Importantly, our adapters are trained on "frozen videos" (i.e., repeated images), constructed from image samples generated by the customized T2I model. This training is facilitated by a novel Motion Adapter module, which allows us to train on such static videos while preserving the motion prior of the video model. At test time, we remove the Motion Adapter modules and leave in only the trained Spatial Adapters. This restores the motion prior of the T2V model while adhering to the spatial prior of the customized T2I model. We demonstrate the effectiveness of our approach on diverse tasks including personalized, stylized, and conditional generation. In all evaluated scenarios, our method seamlessly integrates the spatial prior of the customized T2I model with a motion prior supplied by the T2V model.

AK

40,485 görüntüleme • 2 yıl önce

We are excited to share our work “Event-Aided Sharp Radiance Field Reconstruction for Fast-Flying Drones” published in IEEE Transactions on Robotics IEEE Transactions on Robotics (T-RO), which tackles sharp radiance field reconstruction under agile drone motion, where RGB frames are heavily motion-blurred and pose priors become unreliable! 4 years in the making! Code & dataset released! PDF: Code & Dataset: Full Narrated Video: High-speed flight is essential for time- and battery-constrained missions (e.g., inspection, exploration, search & rescue). However, fast motion corrupts visual data with severe motion blur and introduces drift/noise in visual-inertial odometry, making NeRF-based 3D reconstruction particularly brittle. We propose a unified framework that leverages asynchronous #EventCamera streams together with motion-blurred frames to reconstruct high-fidelity radiance fields from agile drone flights. Our key idea is to embed event-image fusion directly into radiance field optimization while jointly refining a shared, continuous-time camera trajectory initialized from event-based VIO. This enables us to recover sharp radiance fields and accurate trajectories without ground-truth supervision during training. We validate our method on synthetic data and on real sequences captured by a drone flying up to 2 m/s. Despite severe blur and noisy pose priors, our method preserves fine scene details and achieves a performance gain of over 50% on real-world data compared to state-of-the-art methods. Kudos to Rong Zou and Marco Cannici! Marco Cannici Reference: Rong Zou*, Marco Cannici*, Davide Scaramuzza Event-Aided Sharp Radiance Field Reconstruction for Fast-Flying Drones IEEE Transactions on Robotics (T-RO), 2026 NCCR Robotics European Research Council (ERC) AUTOASSESS UZH IfI University of Zurich UZH Science Prophesee SynSense UZH Space Hub
1:02

We are excited to share our work “Event-Aided Sharp Radiance Field Reconstruction for Fast-Flying Drones” published in IEEE Transactions on Robotics IEEE Transactions on Robotics (T-RO), which tackles sharp radiance field reconstruction under agile drone motion, where RGB frames are heavily motion-blurred and pose priors become unreliable! 4 years in the making! Code & dataset released! PDF: Code & Dataset: Full Narrated Video: High-speed flight is essential for time- and battery-constrained missions (e.g., inspection, exploration, search & rescue). However, fast motion corrupts visual data with severe motion blur and introduces drift/noise in visual-inertial odometry, making NeRF-based 3D reconstruction particularly brittle. We propose a unified framework that leverages asynchronous #EventCamera streams together with motion-blurred frames to reconstruct high-fidelity radiance fields from agile drone flights. Our key idea is to embed event-image fusion directly into radiance field optimization while jointly refining a shared, continuous-time camera trajectory initialized from event-based VIO. This enables us to recover sharp radiance fields and accurate trajectories without ground-truth supervision during training. We validate our method on synthetic data and on real sequences captured by a drone flying up to 2 m/s. Despite severe blur and noisy pose priors, our method preserves fine scene details and achieves a performance gain of over 50% on real-world data compared to state-of-the-art methods. Kudos to Rong Zou and Marco Cannici! Marco Cannici Reference: Rong Zou*, Marco Cannici*, Davide Scaramuzza Event-Aided Sharp Radiance Field Reconstruction for Fast-Flying Drones IEEE Transactions on Robotics (T-RO), 2026 NCCR Robotics European Research Council (ERC) AUTOASSESS UZH IfI University of Zurich UZH Science Prophesee SynSense UZH Space Hub

Davide Scaramuzza

12,028 görüntüleme • 6 ay önce

What a ride! Made by using GPT Image 2 + Seedance 2.0 on Fish Creative Prompt reference_handling: "Image generation strictly for driver facial and wardrobe styling reference only — calm, composed features silver wristwatch on left wrist Image strictly for sports car styling and cabin reference only — low, wide Italian wedge-shaped body + bright yellow paint + strongly geometric body lines + hexagonal front intake + Y-shaped LED headlights + gloss black multi-spoke wheels + black leather cabin with orange stitching + left-hand-drive cabin (driver seat on left) + across this sequence the driver-side window (left side of car) is rolled down only for the cockpit reveal shot, all other windows remain as-is throughout. Image strictly for spire architectural geometry, Dubai downtown skyline, and warm hazy midday atmosphere reference only — tapered glass-and-steel spire that widens progressively toward the base + dense glass high-rise skyline below + wide multi-lane boulevard. Do not reproduce any specific camera angle, composition, or caption elements from the reference images" style: "REAL AERIAL + AUTOMOTIVE CINEMATOGRAPHY PLATE — not CGI rendering, not game-engine rendering, not an animated/illustrated look." visual_feel: "Strong overhead midday light + warm hazy atmosphere softening the horizon. Color strictly natural and true-to-life — not oversaturated, not faded, not washed out. Continuous soft haze and atmospheric layering from spire tip down to street level. Every camera move, whether aerial or ground-tracking, strictly gimbal-level smooth — absolutely no handheld feel, no shake, no roll or tilt, even during the FPV-paced dive segment or the accelerating side-pass segments. 16:9 frame + no stylized film-grain treatment, aiming for genuine cinematography texture across every shot" duration: "30 seconds (8-shot sequence)" aspect_ratio: "16:9" character_modeling: driver_suited_woman: base: " appearance and wardrobe strictly per Image generation reference. Present in the car throughout the sequence, but the face is strictly clearly visible only during the 0:20–0:21 cockpit reveal shot — in every other shot the face is strictly not shown or not resolvable, whether by camera position, angle, or framing" wardrobe: silver wristwatch on left wrist . complete, with no wrinkling, misalignment, or missing pieces throughout" presence: "In shots where the driver is not the subject (0:01–0:19, 0:22–0:30), the driver strictly remains seated in the left-side driving position, present but strictly not resolved facially due to camera side, distance, or angle. During the 0:20–0:21 cockpit reveal shot only, the face and posture are strictly fully clear — visibility achieved via a right-side cockpit camera position looking across the cabin, with natural light and open sightline entering through the already-lowered driver-side window (left side of car) forming an angled depth-of-view channel — strictly NOT via looking directly through a window immediately adjacent to the camera" sports_car_yellow: identity: "Low, wide Italian wedge-shaped supercar + bright yellow paint + strongly geometric body + hexagonal front intake + Y-shaped LED headlights + gloss black multi-spoke wheels + black leather cabin with orange stitching + left-hand-drive cabin, driver seat on left — appearance strictly per image2 reference. Strictly only this one car appears across all 8 shots + doors strictly closed throughout + strictly only the driver-side window (left side of car) is rolled down, and only for the 0:20–0:21 cockpit shot + all other windows strictly remain closed/unchanged throughout + left-hand-drive position strictly remains on the left side of the car in every shot — not mirrored, not flipped, regardless of which side the camera is on" physics: "In every shot showing the car in motion, tires strictly show real, visible load deformation on turns + suspension strictly compresses and rebounds continuously with road surface undulation + body strictly shows slight roll and pitch matching cornering or acceleration — strictly not a rigid-glide, zero-deformation model feel. The car strictly stays lane-centered along the boulevard's true path — strictly no crossing lines, no drifting, no hugging the curb" environment_spire_and_skyline: setting_lock: "Tapered glass-and-steel spire structure — body progressively widens toward the base + spire tip is the sequence's starting point + below is Dubai downtown's dense glass high-rise skyline and wide multi-lane boulevard + warm hazy midday light, architectural geometry, skyline, and atmosphere strictly per image3 reference. Shot 1 (0:01–0:12) strictly covers the spire exterior and the high-altitude-to-street transition. Shots 2–8 (0:12–0:30) strictly take place entirely at street level, on or beside the boulevard, with the skyline visible as background context only" cinematic_storyboard: shot_1_spire_descent_0_01_0_12: camera: "Continuous aerial dive — the same FPV-paced descent arc as the master establishing move: 0:01–0:02 camera approaches and briefly hovers directly above the spire tip, gimbal strictly steady, no roll or tilt. 0:02–0:12 camera descends along one continuous curved arc, vertical speed component smoothly decaying while horizontal speed component smoothly increasing, no perceptible docking point or speed jump. As the arc resolves near street level, the camera settles into a position that spotlights the yellow car on the right side of frame — car held in the right third of the composition as the shot closes. Lens strictly 35–50mm cine prime throughout, no zoom, no digital zoom, straight architectural lines keep true perspective." action: "Spire tip and city grid fill the frame at the open, then dissolve into recognizable streets and blocks as the dive continues. The yellow car appears as a small point mid-descent and grows continuously larger, coming to rest spotlighted on the right side of frame by 0:12, driving forward along the boulevard, wheels rotating forward, no reverse." lighting: "Strong overhead midday light at the spire tip with long shadows; as the descent continues, glass-facade and ground reflections shift continuously and smoothly with the changing angle — no abrupt lens-flare flicker." vfx: "Ground detail and color progressively sharpen through the descent, no sudden clarity jump. Ground shadows strictly limited to the car's own cast shadow — no operator or camera-rig shadow anywhere in frame." sfx: "High-altitude wind roar at the open, fading continuously into rising engine sound and city ambience as the car comes into view. No music, no voiceover, no captions." shot_2_side_pass_0_12_0_15: camera: "Hard cut to a static lateral profile position — camera holds a fixed side-view framing of the car, 35–50mm cine prime, gimbal-locked, no handheld sway. As the car accelerates, the camera lets it pull ahead and overtake past the camera's position, exiting frame screen-right." action: "Car holds briefly in profile, then accelerates hard — visible squat of the rear suspension under acceleration, tires gripping without slip, body pitching slightly rearward under load — before overtaking and leaving frame past the camera." lighting: "Even natural daylight, sun still overhead-midday, car's yellow paint reading true and saturated against the boulevard背景, no flat frontal wash." vfx: "Real suspension compression and rebound as the car surges forward. No motion blur artifacts beyond natural shutter response; no CG float." sfx: "Engine note rises sharply with the acceleration, a clean Doppler pass as the car overtakes the camera position; no music." shot_3_center_mirror_0_15_0_17: camera: "Hard cut to an interior point-of-view through the car's center rear-view mirror — camera framed as if looking through the mirror glass from just behind/above the driver's eyeline, mirror surface visibly framing the receding view." action: "Through the mirror, the boulevard and the Dubai skyline recede behind the car as it continues forward at speed; slight natural mirror-glass vignette at the frame edge." lighting: "Cabin interior in soft ambient light, mirror glass reflecting the bright exterior daylight and skyline without glare washing out the reflected image." vfx: "Mirror reflection stays optically clean and stable — no double image, no warping; road and skyline motion in the reflection reads as physically continuous with forward travel." sfx: "Muffled cabin-interior tone to engine and wind noise (heard as if from inside the car); no music, no dialogue." shot_4_front_view_0_18_0_19: camera: "Hard cut to a nose-on front view of the car — camera positioned directly ahead on the boulevard, framing the grille, headlights, and hood centered in frame, lens 35–50mm cine prime, static or minimal push, gimbal-steady." action: "Car approaches head-on at a steady, controlled speed, Y-shaped LED headlights and hexagonal intake clearly readable, wheels visibly rotating forward." lighting: "Overhead midday sun catches the hood and windshield with clean natural highlights, no artificial front-fill look." vfx: "Subtle heat-haze shimmer off the hot asphalt ahead of the car for realism; no CG gloss on the paint." sfx: "Engine sound growing louder as the car closes distance toward camera; no music." shot_5_cockpit_reveal_0_20_0_21: camera: "Hard cut to a right-side cockpit angle — camera positioned to the right-front of the car, sightline crossing through the windshield and, aided by the already-lowered driver-side (left) window, resolving the driver clearly inside the cabin. This is the sequence's only driver-reveal shot." action: "Driver's face and posture are fully visible — one hand resting lightly on the wheel, eyes on the road ahead, expression calm and composed, natural unstiff posture. Car maintains the same forward direction and steady speed with no lens or vehicle behavior change during the shot." lighting: "Even natural daylight, light falling cleanly across the yellow paint, windshield, and driver's face; windshield reflection kept light enough not to obscure visibility." vfx: "Windshield glass stays transparent and reflection-light, no glare occlusion of the driver." sfx: "Steady engine hum plus faint city ambience; strictly no dramatic sound swell or music entering at the reveal moment." shot_6_straight_road_rear_3_4_0_22_0_24: camera: "Hard cut to a rear-bumper 3/4 angle — camera positioned low and behind, off to one side, framing the car driving away down a straight stretch of boulevard, lens 35–50mm cine prime, gimbal-smooth tracking that holds pace with the car." action: "Car drives straight down the boulevard at a steady cruising speed, lane-centered, taillights and rear three-quarter bodywork clearly visible, wheels rotating forward, no drift or lane departure." lighting: "Overhead midday sun, road surface and rear bodywork evenly lit, skyline visible in soft haze in the background." vfx: "Light heat-shimmer off the straight road surface; ground shadow strictly limited to the car's own cast shadow." sfx: "Steady, sustained engine tone at cruising speed plus ambient city sound; no music." shot_7_side_pass_0_25_0_28: camera: "Hard cut back to a static lateral profile position, mirroring shot 2's setup — camera holds the side view of the car, gimbal-locked, no handheld sway." action: "Car holds briefly in profile again, then accelerates a second time and overtakes past the camera, exiting frame — same physical behavior as the first side-pass (visible suspension squat, tire grip, body pitch)." lighting: "Consistent overhead midday daylight, same natural exposure as shot 2 for continuity." vfx: "Real suspension compression and rebound under acceleration; clean natural motion, no CG float." sfx: "Engine note rising sharply into the pass, clean Doppler effect as the car overtakes camera; no music." shot_8_static_3_4_close_0_29_0_30: camera: "Hard cut to a static, locked-off 3/4 angle — camera fixed in position, no movement, gimbal-perfect stillness, framing a 3/4 view of the boulevard as the car enters and exits frame to close the sequence." action: "Car drives through the static frame at a steady speed and exits, completing the sequence; wheels rotating forward, no reverse, no lingering hold after exit." lighting: "Same consistent overhead midday daylight and natural color grade as the rest of the sequence, no shift in exposure for the closing shot." vfx: "Ground shadow strictly limited to the car's own cast shadow; no operator or rig shadow in frame." sfx: "Engine sound passing through and fading as the car exits frame; no music, no voiceover, no captions at any point in the closing shot." production_notes: multi_shot_cut_lock: "This sequence is strictly 8 distinct shots joined by hard cuts at the following points: 0:12, 0:15, 0:18, 0:20, 0:22, 0:25, 0:29 — strictly no smooth transitions, no cross-dissolves, no whip-pans between shots, no morphing between camera setups. Each shot is a clean cut to a new fixed or moving camera setup as specified; only shot 1 (0:01–0:12) is itself one continuous unbroken aerial move. Across every cut, the following must remain continuous: the car's identity and paint color, the boulevard geography and skyline, the direction of travel, the lighting direction and quality, and the absence of music/dialogue/captions." gimbal_stabilization_lock: "Every shot, aerial or ground-based, strictly holds gimbal-level smoothness — absolutely no handheld shake, no roll, no tilt, no high-frequency jitter, in any of the 8 shots, including the accelerating side-pass shots." optics_lock: "Every ground/tracking shot strictly uses a 35–50mm cinema prime feel (Sony Cine prime lens character), unchanged within each shot — strictly no zoom in, no zoom out, no digital zoom of any kind in any shot. Straight lines of buildings and roads strictly retain true perspective — strictly no wide-angle distortion, no fisheye curvature." forward_motion_lock: "In every shot, the car strictly drives forward — nose pointed in the direction of travel except where explicitly framed nose-on toward camera (shot 4), and never reversing. Wheels strictly rotate continuously in the direction of travel, reverse rotation strictly forbidden in any shot." vehicle_structural_identity_lock: "Strictly only this one car appears across all 8 shots — a second car of the same or different model is strictly forbidden. Doors strictly remain closed in every shot. Left-hand-drive layout strictly remains unchanged across all 8 shots — driver's seat strictly on the left side of the car in every shot, never mirrored or flipped regardless of camera side. The driver-side (left) window is strictly rolled down only during shot 5 (cockpit reveal, 0:20–0:21); in every other shot all windows strictly remain closed/unchanged." driver_appearance_window_lock: "The driver's face is strictly clearly visible only during shot 5 (0:20–0:21) — in shots 1–4 and 6–8 the face is strictly not resolved, whether due to distance, angle, motion, or framing. During shot 5, visibility is strictly achieved via the right-side cockpit camera angle through the windshield, aided by the already-lowered driver-side window — strictly NOT via a window directly facing the camera." no_operator_shadow_lock: "In every shot, the ground strictly shows only the car's own cast shadow — strictly no human-shaped shadow, camera-operator silhouette, photographer's figure, or rig shadow cast anywhere in any of the 8 shots. The aerial shot (shot 1) is strictly pure drone photography with no physical rig or ground-crew trace; the ground shots (2–8) are strictly framed with no visible operator, crew, or equipment in frame." audio_lock: "Sound throughout the sequence is strictly authentic diegetic sound only — wind, engine, and city ambience, shifting naturally shot to shot — strictly no background music or score of any kind, including any hidden musical layer, in any of the 8 shots + strictly no voiceover or dialogue of any kind + strictly no captions or on-screen text of any kind at any point." critical_constraint: "8 hard-cut shots across 30 seconds, cut points strictly at 0:12, 0:15, 0:18, 0:20, 0:22, 0:25, 0:29 — strictly no dissolves or blended transitions. Shot 1 is one continuous uncut aerial descent from the spire tip to street level, ending with the car spotlighted screen-right. Shots 2 and 7 are matching static side-profile setups where the car accelerates and passes the camera. Shot 5 is the sequence's only driver-face reveal, via the right-side cockpit angle through the windshield with the driver-side window down — strictly not visible in any other shot. The car is strictly the same single yellow LHD supercar throughout, doors closed except for the driver-side window during shot 5, always driving forward, wheels never reversing. All camera work is strictly gimbal-smooth with a 35–50mm cine-prime feel, no zoom, no distortion, no handheld shake in any shot. Ground shadows throughout strictly show only the car's own shadow, no operator or rig trace. Audio is strictly diegetic only — no music, no voiceover, no captions, throughout the entire 30 seconds." avoid: "dissolves, cross-fades, morphs, or whip-pans between shots — cuts must be hard cuts only, any shot other than shot 5 showing the driver's face clearly, the driver-side window rolled down in any shot other than shot 5, the passenger-side or any other window rolled down at any point, a second car appearing in any shot, car doors opening in any shot, the car reversing or wheels rotating backward in any shot, mirrored or flipped left-hand-drive layout, driver's seat appearing on the right side of the car, human-shaped shadow, camera-operator silhouette, or rig shadow in any of the 8 shots, camera shake, handheld feel, roll, tilt, or high-frequency jitter in any shot, zoom in, zoom out, digital zoom, wide-angle distortion, fisheye distortion, or curved building/road lines in any shot, smooth continuous single-take treatment of the whole 30 seconds (the sequence is strictly multi-shot with hard cuts, not one unbroken take beyond shot 1), background music, score, melody, hidden musical layer, voiceover, dialogue, narration, captions, or on-screen text at any point, CGI-rendered look, game-engine feel, plasticky car-paint gloss, static hovering with no sense of gravity" animation_style: "Shot 1 plays out as one continuous real-time aerial move; shots 2–8 are each a distinct, clean hard-cut setup, every shot internally in real time with no speed ramping — the sense of pace across the sequence comes from the editing rhythm of the cuts, not from slow motion or time manipulation within any single shot"
0:30

What a ride! Made by using GPT Image 2 + Seedance 2.0 on Fish Creative Prompt reference_handling: "Image generation strictly for driver facial and wardrobe styling reference only — calm, composed features silver wristwatch on left wrist Image strictly for sports car styling and cabin reference only — low, wide Italian wedge-shaped body + bright yellow paint + strongly geometric body lines + hexagonal front intake + Y-shaped LED headlights + gloss black multi-spoke wheels + black leather cabin with orange stitching + left-hand-drive cabin (driver seat on left) + across this sequence the driver-side window (left side of car) is rolled down only for the cockpit reveal shot, all other windows remain as-is throughout. Image strictly for spire architectural geometry, Dubai downtown skyline, and warm hazy midday atmosphere reference only — tapered glass-and-steel spire that widens progressively toward the base + dense glass high-rise skyline below + wide multi-lane boulevard. Do not reproduce any specific camera angle, composition, or caption elements from the reference images" style: "REAL AERIAL + AUTOMOTIVE CINEMATOGRAPHY PLATE — not CGI rendering, not game-engine rendering, not an animated/illustrated look." visual_feel: "Strong overhead midday light + warm hazy atmosphere softening the horizon. Color strictly natural and true-to-life — not oversaturated, not faded, not washed out. Continuous soft haze and atmospheric layering from spire tip down to street level. Every camera move, whether aerial or ground-tracking, strictly gimbal-level smooth — absolutely no handheld feel, no shake, no roll or tilt, even during the FPV-paced dive segment or the accelerating side-pass segments. 16:9 frame + no stylized film-grain treatment, aiming for genuine cinematography texture across every shot" duration: "30 seconds (8-shot sequence)" aspect_ratio: "16:9" character_modeling: driver_suited_woman: base: " appearance and wardrobe strictly per Image generation reference. Present in the car throughout the sequence, but the face is strictly clearly visible only during the 0:20–0:21 cockpit reveal shot — in every other shot the face is strictly not shown or not resolvable, whether by camera position, angle, or framing" wardrobe: silver wristwatch on left wrist . complete, with no wrinkling, misalignment, or missing pieces throughout" presence: "In shots where the driver is not the subject (0:01–0:19, 0:22–0:30), the driver strictly remains seated in the left-side driving position, present but strictly not resolved facially due to camera side, distance, or angle. During the 0:20–0:21 cockpit reveal shot only, the face and posture are strictly fully clear — visibility achieved via a right-side cockpit camera position looking across the cabin, with natural light and open sightline entering through the already-lowered driver-side window (left side of car) forming an angled depth-of-view channel — strictly NOT via looking directly through a window immediately adjacent to the camera" sports_car_yellow: identity: "Low, wide Italian wedge-shaped supercar + bright yellow paint + strongly geometric body + hexagonal front intake + Y-shaped LED headlights + gloss black multi-spoke wheels + black leather cabin with orange stitching + left-hand-drive cabin, driver seat on left — appearance strictly per image2 reference. Strictly only this one car appears across all 8 shots + doors strictly closed throughout + strictly only the driver-side window (left side of car) is rolled down, and only for the 0:20–0:21 cockpit shot + all other windows strictly remain closed/unchanged throughout + left-hand-drive position strictly remains on the left side of the car in every shot — not mirrored, not flipped, regardless of which side the camera is on" physics: "In every shot showing the car in motion, tires strictly show real, visible load deformation on turns + suspension strictly compresses and rebounds continuously with road surface undulation + body strictly shows slight roll and pitch matching cornering or acceleration — strictly not a rigid-glide, zero-deformation model feel. The car strictly stays lane-centered along the boulevard's true path — strictly no crossing lines, no drifting, no hugging the curb" environment_spire_and_skyline: setting_lock: "Tapered glass-and-steel spire structure — body progressively widens toward the base + spire tip is the sequence's starting point + below is Dubai downtown's dense glass high-rise skyline and wide multi-lane boulevard + warm hazy midday light, architectural geometry, skyline, and atmosphere strictly per image3 reference. Shot 1 (0:01–0:12) strictly covers the spire exterior and the high-altitude-to-street transition. Shots 2–8 (0:12–0:30) strictly take place entirely at street level, on or beside the boulevard, with the skyline visible as background context only" cinematic_storyboard: shot_1_spire_descent_0_01_0_12: camera: "Continuous aerial dive — the same FPV-paced descent arc as the master establishing move: 0:01–0:02 camera approaches and briefly hovers directly above the spire tip, gimbal strictly steady, no roll or tilt. 0:02–0:12 camera descends along one continuous curved arc, vertical speed component smoothly decaying while horizontal speed component smoothly increasing, no perceptible docking point or speed jump. As the arc resolves near street level, the camera settles into a position that spotlights the yellow car on the right side of frame — car held in the right third of the composition as the shot closes. Lens strictly 35–50mm cine prime throughout, no zoom, no digital zoom, straight architectural lines keep true perspective." action: "Spire tip and city grid fill the frame at the open, then dissolve into recognizable streets and blocks as the dive continues. The yellow car appears as a small point mid-descent and grows continuously larger, coming to rest spotlighted on the right side of frame by 0:12, driving forward along the boulevard, wheels rotating forward, no reverse." lighting: "Strong overhead midday light at the spire tip with long shadows; as the descent continues, glass-facade and ground reflections shift continuously and smoothly with the changing angle — no abrupt lens-flare flicker." vfx: "Ground detail and color progressively sharpen through the descent, no sudden clarity jump. Ground shadows strictly limited to the car's own cast shadow — no operator or camera-rig shadow anywhere in frame." sfx: "High-altitude wind roar at the open, fading continuously into rising engine sound and city ambience as the car comes into view. No music, no voiceover, no captions." shot_2_side_pass_0_12_0_15: camera: "Hard cut to a static lateral profile position — camera holds a fixed side-view framing of the car, 35–50mm cine prime, gimbal-locked, no handheld sway. As the car accelerates, the camera lets it pull ahead and overtake past the camera's position, exiting frame screen-right." action: "Car holds briefly in profile, then accelerates hard — visible squat of the rear suspension under acceleration, tires gripping without slip, body pitching slightly rearward under load — before overtaking and leaving frame past the camera." lighting: "Even natural daylight, sun still overhead-midday, car's yellow paint reading true and saturated against the boulevard背景, no flat frontal wash." vfx: "Real suspension compression and rebound as the car surges forward. No motion blur artifacts beyond natural shutter response; no CG float." sfx: "Engine note rises sharply with the acceleration, a clean Doppler pass as the car overtakes the camera position; no music." shot_3_center_mirror_0_15_0_17: camera: "Hard cut to an interior point-of-view through the car's center rear-view mirror — camera framed as if looking through the mirror glass from just behind/above the driver's eyeline, mirror surface visibly framing the receding view." action: "Through the mirror, the boulevard and the Dubai skyline recede behind the car as it continues forward at speed; slight natural mirror-glass vignette at the frame edge." lighting: "Cabin interior in soft ambient light, mirror glass reflecting the bright exterior daylight and skyline without glare washing out the reflected image." vfx: "Mirror reflection stays optically clean and stable — no double image, no warping; road and skyline motion in the reflection reads as physically continuous with forward travel." sfx: "Muffled cabin-interior tone to engine and wind noise (heard as if from inside the car); no music, no dialogue." shot_4_front_view_0_18_0_19: camera: "Hard cut to a nose-on front view of the car — camera positioned directly ahead on the boulevard, framing the grille, headlights, and hood centered in frame, lens 35–50mm cine prime, static or minimal push, gimbal-steady." action: "Car approaches head-on at a steady, controlled speed, Y-shaped LED headlights and hexagonal intake clearly readable, wheels visibly rotating forward." lighting: "Overhead midday sun catches the hood and windshield with clean natural highlights, no artificial front-fill look." vfx: "Subtle heat-haze shimmer off the hot asphalt ahead of the car for realism; no CG gloss on the paint." sfx: "Engine sound growing louder as the car closes distance toward camera; no music." shot_5_cockpit_reveal_0_20_0_21: camera: "Hard cut to a right-side cockpit angle — camera positioned to the right-front of the car, sightline crossing through the windshield and, aided by the already-lowered driver-side (left) window, resolving the driver clearly inside the cabin. This is the sequence's only driver-reveal shot." action: "Driver's face and posture are fully visible — one hand resting lightly on the wheel, eyes on the road ahead, expression calm and composed, natural unstiff posture. Car maintains the same forward direction and steady speed with no lens or vehicle behavior change during the shot." lighting: "Even natural daylight, light falling cleanly across the yellow paint, windshield, and driver's face; windshield reflection kept light enough not to obscure visibility." vfx: "Windshield glass stays transparent and reflection-light, no glare occlusion of the driver." sfx: "Steady engine hum plus faint city ambience; strictly no dramatic sound swell or music entering at the reveal moment." shot_6_straight_road_rear_3_4_0_22_0_24: camera: "Hard cut to a rear-bumper 3/4 angle — camera positioned low and behind, off to one side, framing the car driving away down a straight stretch of boulevard, lens 35–50mm cine prime, gimbal-smooth tracking that holds pace with the car." action: "Car drives straight down the boulevard at a steady cruising speed, lane-centered, taillights and rear three-quarter bodywork clearly visible, wheels rotating forward, no drift or lane departure." lighting: "Overhead midday sun, road surface and rear bodywork evenly lit, skyline visible in soft haze in the background." vfx: "Light heat-shimmer off the straight road surface; ground shadow strictly limited to the car's own cast shadow." sfx: "Steady, sustained engine tone at cruising speed plus ambient city sound; no music." shot_7_side_pass_0_25_0_28: camera: "Hard cut back to a static lateral profile position, mirroring shot 2's setup — camera holds the side view of the car, gimbal-locked, no handheld sway." action: "Car holds briefly in profile again, then accelerates a second time and overtakes past the camera, exiting frame — same physical behavior as the first side-pass (visible suspension squat, tire grip, body pitch)." lighting: "Consistent overhead midday daylight, same natural exposure as shot 2 for continuity." vfx: "Real suspension compression and rebound under acceleration; clean natural motion, no CG float." sfx: "Engine note rising sharply into the pass, clean Doppler effect as the car overtakes camera; no music." shot_8_static_3_4_close_0_29_0_30: camera: "Hard cut to a static, locked-off 3/4 angle — camera fixed in position, no movement, gimbal-perfect stillness, framing a 3/4 view of the boulevard as the car enters and exits frame to close the sequence." action: "Car drives through the static frame at a steady speed and exits, completing the sequence; wheels rotating forward, no reverse, no lingering hold after exit." lighting: "Same consistent overhead midday daylight and natural color grade as the rest of the sequence, no shift in exposure for the closing shot." vfx: "Ground shadow strictly limited to the car's own cast shadow; no operator or rig shadow in frame." sfx: "Engine sound passing through and fading as the car exits frame; no music, no voiceover, no captions at any point in the closing shot." production_notes: multi_shot_cut_lock: "This sequence is strictly 8 distinct shots joined by hard cuts at the following points: 0:12, 0:15, 0:18, 0:20, 0:22, 0:25, 0:29 — strictly no smooth transitions, no cross-dissolves, no whip-pans between shots, no morphing between camera setups. Each shot is a clean cut to a new fixed or moving camera setup as specified; only shot 1 (0:01–0:12) is itself one continuous unbroken aerial move. Across every cut, the following must remain continuous: the car's identity and paint color, the boulevard geography and skyline, the direction of travel, the lighting direction and quality, and the absence of music/dialogue/captions." gimbal_stabilization_lock: "Every shot, aerial or ground-based, strictly holds gimbal-level smoothness — absolutely no handheld shake, no roll, no tilt, no high-frequency jitter, in any of the 8 shots, including the accelerating side-pass shots." optics_lock: "Every ground/tracking shot strictly uses a 35–50mm cinema prime feel (Sony Cine prime lens character), unchanged within each shot — strictly no zoom in, no zoom out, no digital zoom of any kind in any shot. Straight lines of buildings and roads strictly retain true perspective — strictly no wide-angle distortion, no fisheye curvature." forward_motion_lock: "In every shot, the car strictly drives forward — nose pointed in the direction of travel except where explicitly framed nose-on toward camera (shot 4), and never reversing. Wheels strictly rotate continuously in the direction of travel, reverse rotation strictly forbidden in any shot." vehicle_structural_identity_lock: "Strictly only this one car appears across all 8 shots — a second car of the same or different model is strictly forbidden. Doors strictly remain closed in every shot. Left-hand-drive layout strictly remains unchanged across all 8 shots — driver's seat strictly on the left side of the car in every shot, never mirrored or flipped regardless of camera side. The driver-side (left) window is strictly rolled down only during shot 5 (cockpit reveal, 0:20–0:21); in every other shot all windows strictly remain closed/unchanged." driver_appearance_window_lock: "The driver's face is strictly clearly visible only during shot 5 (0:20–0:21) — in shots 1–4 and 6–8 the face is strictly not resolved, whether due to distance, angle, motion, or framing. During shot 5, visibility is strictly achieved via the right-side cockpit camera angle through the windshield, aided by the already-lowered driver-side window — strictly NOT via a window directly facing the camera." no_operator_shadow_lock: "In every shot, the ground strictly shows only the car's own cast shadow — strictly no human-shaped shadow, camera-operator silhouette, photographer's figure, or rig shadow cast anywhere in any of the 8 shots. The aerial shot (shot 1) is strictly pure drone photography with no physical rig or ground-crew trace; the ground shots (2–8) are strictly framed with no visible operator, crew, or equipment in frame." audio_lock: "Sound throughout the sequence is strictly authentic diegetic sound only — wind, engine, and city ambience, shifting naturally shot to shot — strictly no background music or score of any kind, including any hidden musical layer, in any of the 8 shots + strictly no voiceover or dialogue of any kind + strictly no captions or on-screen text of any kind at any point." critical_constraint: "8 hard-cut shots across 30 seconds, cut points strictly at 0:12, 0:15, 0:18, 0:20, 0:22, 0:25, 0:29 — strictly no dissolves or blended transitions. Shot 1 is one continuous uncut aerial descent from the spire tip to street level, ending with the car spotlighted screen-right. Shots 2 and 7 are matching static side-profile setups where the car accelerates and passes the camera. Shot 5 is the sequence's only driver-face reveal, via the right-side cockpit angle through the windshield with the driver-side window down — strictly not visible in any other shot. The car is strictly the same single yellow LHD supercar throughout, doors closed except for the driver-side window during shot 5, always driving forward, wheels never reversing. All camera work is strictly gimbal-smooth with a 35–50mm cine-prime feel, no zoom, no distortion, no handheld shake in any shot. Ground shadows throughout strictly show only the car's own shadow, no operator or rig trace. Audio is strictly diegetic only — no music, no voiceover, no captions, throughout the entire 30 seconds." avoid: "dissolves, cross-fades, morphs, or whip-pans between shots — cuts must be hard cuts only, any shot other than shot 5 showing the driver's face clearly, the driver-side window rolled down in any shot other than shot 5, the passenger-side or any other window rolled down at any point, a second car appearing in any shot, car doors opening in any shot, the car reversing or wheels rotating backward in any shot, mirrored or flipped left-hand-drive layout, driver's seat appearing on the right side of the car, human-shaped shadow, camera-operator silhouette, or rig shadow in any of the 8 shots, camera shake, handheld feel, roll, tilt, or high-frequency jitter in any shot, zoom in, zoom out, digital zoom, wide-angle distortion, fisheye distortion, or curved building/road lines in any shot, smooth continuous single-take treatment of the whole 30 seconds (the sequence is strictly multi-shot with hard cuts, not one unbroken take beyond shot 1), background music, score, melody, hidden musical layer, voiceover, dialogue, narration, captions, or on-screen text at any point, CGI-rendered look, game-engine feel, plasticky car-paint gloss, static hovering with no sense of gravity" animation_style: "Shot 1 plays out as one continuous real-time aerial move; shots 2–8 are each a distinct, clean hard-cut setup, every shot internally in real time with no speed ramping — the sense of pace across the sequence comes from the editing rhythm of the cuts, not from slow motion or time manipulation within any single shot"

Aaliya

11,134 görüntüleme • 12 gün önce

NVIDIA just unleashed SANA-WM and it’s an absolute MONSTER for the future of open source AI! A blazing-fast 2.6B-parameter open-source world model that doesn’t just generate video… it creates controllable, physics-rich, high-fidelity worlds on demand. Why this is insanely powerful: • One image + text prompt + 6-DoF camera trajectory → generates 720p videos up to 60 seconds long with buttery-smooth, precisely controlled camera movement. You’re not just watching, you’re piloting the simulation. • Runs locally on a single consumer GPU (RTX 5090 level) thanks to heavy distillation + NVFP4 quantization. Full 60-second clip denoised in ~34 seconds. No massive clusters required. • 36× higher throughput than previous open models while rivaling (or beating) closed industrial giants in visual quality and consistency. • Trained lightning-fast: ~213K public videos in just 15 days on 64 H100s. • Built with next-level tech: Hybrid Linear Attention, dual-branch camera control, two-stage pipeline, and rock-solid metric-scale pose understanding. This is a true open world model, the foundation for embodied AI, robotics, autonomous systems, and hyper-realistic simulations that can run anywhere. Project: At our Zero-Human Company, we’re already running SANA-WM live in our core pipelines. It’s supercharging autonomous agent training, generating unlimited synthetic training data, and powering full end-to-end simulation loops, zero humans in the loop. The speed and control let us test thousands of edge-case scenarios overnight, iterate at lightspeed, and push our fully autonomous operations further than ever before. This is the kind of breakthrough that turns science fiction into daily reality. World models just leveled up — hard. The age of personal, local, controllable universes is here.
1:13

NVIDIA just unleashed SANA-WM and it’s an absolute MONSTER for the future of open source AI! A blazing-fast 2.6B-parameter open-source world model that doesn’t just generate video… it creates controllable, physics-rich, high-fidelity worlds on demand. Why this is insanely powerful: • One image + text prompt + 6-DoF camera trajectory → generates 720p videos up to 60 seconds long with buttery-smooth, precisely controlled camera movement. You’re not just watching, you’re piloting the simulation. • Runs locally on a single consumer GPU (RTX 5090 level) thanks to heavy distillation + NVFP4 quantization. Full 60-second clip denoised in ~34 seconds. No massive clusters required. • 36× higher throughput than previous open models while rivaling (or beating) closed industrial giants in visual quality and consistency. • Trained lightning-fast: ~213K public videos in just 15 days on 64 H100s. • Built with next-level tech: Hybrid Linear Attention, dual-branch camera control, two-stage pipeline, and rock-solid metric-scale pose understanding. This is a true open world model, the foundation for embodied AI, robotics, autonomous systems, and hyper-realistic simulations that can run anywhere. Project: At our Zero-Human Company, we’re already running SANA-WM live in our core pipelines. It’s supercharging autonomous agent training, generating unlimited synthetic training data, and powering full end-to-end simulation loops, zero humans in the loop. The speed and control let us test thousands of edge-case scenarios overnight, iterate at lightspeed, and push our fully autonomous operations further than ever before. This is the kind of breakthrough that turns science fiction into daily reality. World models just leveled up — hard. The age of personal, local, controllable universes is here.

Brian Roemmele

619,208 görüntüleme • 3 ay önce

Your entire media library, searchable by memory instead of filename, running right on your own machine. It’s a local-first AI file agent called MUZIM. Instead of managing folders, you work with what's actually inside your photos, videos, and documents. Here's how it comes together in a real workflow: You get back from a two-week trip with 2,000 photos and clips on your camera. You point MUZIM at the folder, and it builds Collections on its own, by place, by people, by day. Nothing gets moved from where it already lives. A week later you want "the sunset shot where everyone's laughing." You don't remember the filename. So you just describe it. Vibe Search understands what's inside your media and takes you straight to that moment, even the exact second inside a video. Once the local model is downloaded, this part runs fully offline, connected or not. Then you turn it into something. Connect your own Claude or GPT key, and the online AI Agent pulls the best shots from that trip and drafts a highlight video, an X thread, or a recap. Through MCP, compatible tools work with authorized context without re-uploading gigabytes of raw video - saving massive input tokens while keeping raw files strictly local. The whole idea: search and organization run locally, on your device the AI workflows run when you want them, on your own key your raw files never leave your machine Cold storage becomes a private workspace you actually control.
1:04

Your entire media library, searchable by memory instead of filename, running right on your own machine. It’s a local-first AI file agent called MUZIM. Instead of managing folders, you work with what's actually inside your photos, videos, and documents. Here's how it comes together in a real workflow: You get back from a two-week trip with 2,000 photos and clips on your camera. You point MUZIM at the folder, and it builds Collections on its own, by place, by people, by day. Nothing gets moved from where it already lives. A week later you want "the sunset shot where everyone's laughing." You don't remember the filename. So you just describe it. Vibe Search understands what's inside your media and takes you straight to that moment, even the exact second inside a video. Once the local model is downloaded, this part runs fully offline, connected or not. Then you turn it into something. Connect your own Claude or GPT key, and the online AI Agent pulls the best shots from that trip and drafts a highlight video, an X thread, or a recap. Through MCP, compatible tools work with authorized context without re-uploading gigabytes of raw video - saving massive input tokens while keeping raw files strictly local. The whole idea: search and organization run locally, on your device the AI workflows run when you want them, on your own key your raw files never leave your machine Cold storage becomes a private workspace you actually control.

Alvaro Cintas

80,587 görüntüleme • 19 gün önce

The U.S. State Department and its 50 years of shadowy ties to GEORGE SOROS. George Soros's Open Society Foundations orchestrated a covert operation in Mongolia, partnering with the US State Department to sabotage a mining deal. Using WikiLeaks cables, Mike Benz reveals OSF funded "civil society" groups and protests to pressure the Mongolian government into renegotiating terms—ultimately benefiting mining giant Rio Tinto, where Soros's hedge fund held massive stakes. This wasn't philanthropy; it was a hedge fund play disguised as NGO activism, stripping Mongolia of fair profits while enriching Soros. But this Mongolian scheme is just one thread in OSF's global web. In the United States, critics argue OSF's activities go far beyond "promoting democracy"—they're systematically undermining American sovereignty, rule of law, and cultural cohesion through billions in funding to progressive causes. This is the breakdown of how OSF is accused of "destroying" the U.S., based on documented patterns. Weaponizing the Justice System via "Progressive" Prosecutors, OSF has poured over $140 million into electing district attorneys who prioritize "criminal justice reform" over enforcement. These Soros-backed DAs, like those in major cities, often decline to prosecute crimes, leading to spikes in urban chaos, repeat offenders, and eroded public safety. This isn't reform—it's intentional destabilization, creating a two-tiered justice system that weakens law and order while advancing a radical agenda. Fueling Mass Migration and Open Borders, OSF funds NGOs that advocate for unchecked immigration, sanctuary cities, and the dismantling of border enforcement. This includes groups pushing for policies that overwhelm social services, alter demographics, and strain resources—critics call it a "Great Replacement" strategy to shift electoral power and erode national identity. By partnering with entities like the Tides Foundation, OSF creates "astroturf" movements that make illegal migration seem like a humanitarian imperative, all while bypassing voter will. Controlling Narratives Through Media and "Fact-Checkers," with investments in journalism training and outlets, OSF shapes public discourse to vilify nationalism and promote globalism. This includes funding "watchdog" groups that censor dissent and amplify progressive ideologies, turning media into a tool for social engineering. In the U.S., this manifests as biased coverage on issues like crime, borders, and elections, eroding trust in institutions and polarizing society. Subverting Elections and Institutions, OSF's opaque, offshore funding structures channel money into voter mobilization, legal challenges, and activist networks that challenge election integrity and push for systemic changes. This mirrors the Mongolian playbook, using "civil society" as a front to influence outcomes for elite gain. In America, it's accused of fostering division through identity politics, DEI initiatives, and cultural subversion, aiming for a "color revolution" that replaces constitutional governance with technocratic control. Partnering with Government for Insider Influence, just like in Mongolia with the State Department, OSF collaborates with U.S. agencies and officials to advance its goals. This includes embedding allies in policy roles, funding "democracy promotion" that aligns with globalist interests, and using taxpayer dollars via grants to NGOs—turning philanthropy into a profit-driven political machine. OSF claims it's building "vibrant democracies," but detractors see a pattern of chaos creation for power consolidation. With $25B+ in assets and operations in 120 countries, this isn't charity—it's a blueprint for dismantling nations from within. Time to expose and defund this shadow empire. Mike Benz
29:56

The U.S. State Department and its 50 years of shadowy ties to GEORGE SOROS. George Soros's Open Society Foundations orchestrated a covert operation in Mongolia, partnering with the US State Department to sabotage a mining deal. Using WikiLeaks cables, Mike Benz reveals OSF funded "civil society" groups and protests to pressure the Mongolian government into renegotiating terms—ultimately benefiting mining giant Rio Tinto, where Soros's hedge fund held massive stakes. This wasn't philanthropy; it was a hedge fund play disguised as NGO activism, stripping Mongolia of fair profits while enriching Soros. But this Mongolian scheme is just one thread in OSF's global web. In the United States, critics argue OSF's activities go far beyond "promoting democracy"—they're systematically undermining American sovereignty, rule of law, and cultural cohesion through billions in funding to progressive causes. This is the breakdown of how OSF is accused of "destroying" the U.S., based on documented patterns. Weaponizing the Justice System via "Progressive" Prosecutors, OSF has poured over $140 million into electing district attorneys who prioritize "criminal justice reform" over enforcement. These Soros-backed DAs, like those in major cities, often decline to prosecute crimes, leading to spikes in urban chaos, repeat offenders, and eroded public safety. This isn't reform—it's intentional destabilization, creating a two-tiered justice system that weakens law and order while advancing a radical agenda. Fueling Mass Migration and Open Borders, OSF funds NGOs that advocate for unchecked immigration, sanctuary cities, and the dismantling of border enforcement. This includes groups pushing for policies that overwhelm social services, alter demographics, and strain resources—critics call it a "Great Replacement" strategy to shift electoral power and erode national identity. By partnering with entities like the Tides Foundation, OSF creates "astroturf" movements that make illegal migration seem like a humanitarian imperative, all while bypassing voter will. Controlling Narratives Through Media and "Fact-Checkers," with investments in journalism training and outlets, OSF shapes public discourse to vilify nationalism and promote globalism. This includes funding "watchdog" groups that censor dissent and amplify progressive ideologies, turning media into a tool for social engineering. In the U.S., this manifests as biased coverage on issues like crime, borders, and elections, eroding trust in institutions and polarizing society. Subverting Elections and Institutions, OSF's opaque, offshore funding structures channel money into voter mobilization, legal challenges, and activist networks that challenge election integrity and push for systemic changes. This mirrors the Mongolian playbook, using "civil society" as a front to influence outcomes for elite gain. In America, it's accused of fostering division through identity politics, DEI initiatives, and cultural subversion, aiming for a "color revolution" that replaces constitutional governance with technocratic control. Partnering with Government for Insider Influence, just like in Mongolia with the State Department, OSF collaborates with U.S. agencies and officials to advance its goals. This includes embedding allies in policy roles, funding "democracy promotion" that aligns with globalist interests, and using taxpayer dollars via grants to NGOs—turning philanthropy into a profit-driven political machine. OSF claims it's building "vibrant democracies," but detractors see a pattern of chaos creation for power consolidation. With $25B+ in assets and operations in 120 countries, this isn't charity—it's a blueprint for dismantling nations from within. Time to expose and defund this shadow empire. Mike Benz

The SCIF

41,240 görüntüleme • 9 ay önce

Thrilled to announce Kingnet AI V2 is now officially live ! We have officially deployed on the BNB Chain first ! Whether you're an enthusiast or a professional game developer, come and try it out now： Each generated asset costs approximately $3 and supports export in professional game-editing formats. We will soon support exporting assets in NFT on-chain formats, empowering Web3 users and partners with seamless integration. Jump down more rabbit holes next.👇 📔 Product Introduction： By conversing naturally with agent Joi, users can achieve a complete automated game development cycle - from requirement proposal to finished product delivery. Users simply need to describe their game concepts and design requirements in natural language, and Joi will automatically utilize built-in generator including: • Animation Generator： AI-driven motion generation with auto-rigging technology for instant character animation • Map Generator： Procedural map generation with built-in logic validation for consistent world-building • Numerical Generator： Automated game economy tuning for fair yet challenging gameplay systems • Editable Code Generator： Generates clean, maintainable game logic code with multi-platform/multi-language support • Interface Generator： Intelligent layout engine that optimizes user experience and interaction flow Joi intelligently generates all necessary game components, performs multi-dimensional feasibility checks, and ultimately completes game synthesis, packaging and deployment. Users can directly click to try the game on the chat interface, or download the complete editable code package to achieve rapid iteration and secondary development. 🎯 Core Architecture： 1/ Natural Language Understanding & Multimodal Intent Parsing: Utilizing advanced deep learning NLP models (e.g., Transformer-based language understanding models), Joi precisely interprets user natural language inputs and extracts core game design intents and parameters. Through semantic segmentation and entity recognition, complex requirements are decomposed into specific tasks for animation, map, numerical systems, UI, and code modules. 2/ Modular Editor System & API Integration: Joi employs a unified API framework to enable seamless collaboration between editor modules, ensuring high compatibility in data formats and workflows. 3/ Intelligent Validation & Quality Assurance: The system incorporates multi-dimensional verification mechanisms including animation continuity checks, map pathfinding and physical logic validation, game balance analysis, UI interaction consistency verification, and static/dynamic code security testing. Automated testing and feedback loops ensure outputs meet high-standard game design specifications. 4/ Automatic Synthesis, Packaging & Instant Deployment: Verified resources are automatically integrated to complete game compilation, packaging and deployment. Supports one-click generation of playable online links and downloadable complete code packages for immediate testing or deep customization/iterative development. 5/ Interactive Chat Interface & Seamless UX: The entire workflow is completed within the chat interface, significantly reducing traditional game development's communication and operational barriers. Users accomplish complex game design and development through conversation while receiving real-time feedback and adjustment suggestions, democratizing game creation. 6/ Industry-Disrupting Value: Transforms traditional manual development into AI-driven automated pipelines.
1:24

Thrilled to announce Kingnet AI V2 is now officially live ! We have officially deployed on the BNB Chain first ! Whether you're an enthusiast or a professional game developer, come and try it out now： Each generated asset costs approximately $3 and supports export in professional game-editing formats. We will soon support exporting assets in NFT on-chain formats, empowering Web3 users and partners with seamless integration. Jump down more rabbit holes next.👇 📔 Product Introduction： By conversing naturally with agent Joi, users can achieve a complete automated game development cycle - from requirement proposal to finished product delivery. Users simply need to describe their game concepts and design requirements in natural language, and Joi will automatically utilize built-in generator including: • Animation Generator： AI-driven motion generation with auto-rigging technology for instant character animation • Map Generator： Procedural map generation with built-in logic validation for consistent world-building • Numerical Generator： Automated game economy tuning for fair yet challenging gameplay systems • Editable Code Generator： Generates clean, maintainable game logic code with multi-platform/multi-language support • Interface Generator： Intelligent layout engine that optimizes user experience and interaction flow Joi intelligently generates all necessary game components, performs multi-dimensional feasibility checks, and ultimately completes game synthesis, packaging and deployment. Users can directly click to try the game on the chat interface, or download the complete editable code package to achieve rapid iteration and secondary development. 🎯 Core Architecture： 1/ Natural Language Understanding & Multimodal Intent Parsing: Utilizing advanced deep learning NLP models (e.g., Transformer-based language understanding models), Joi precisely interprets user natural language inputs and extracts core game design intents and parameters. Through semantic segmentation and entity recognition, complex requirements are decomposed into specific tasks for animation, map, numerical systems, UI, and code modules. 2/ Modular Editor System & API Integration: Joi employs a unified API framework to enable seamless collaboration between editor modules, ensuring high compatibility in data formats and workflows. 3/ Intelligent Validation & Quality Assurance: The system incorporates multi-dimensional verification mechanisms including animation continuity checks, map pathfinding and physical logic validation, game balance analysis, UI interaction consistency verification, and static/dynamic code security testing. Automated testing and feedback loops ensure outputs meet high-standard game design specifications. 4/ Automatic Synthesis, Packaging & Instant Deployment: Verified resources are automatically integrated to complete game compilation, packaging and deployment. Supports one-click generation of playable online links and downloadable complete code packages for immediate testing or deep customization/iterative development. 5/ Interactive Chat Interface & Seamless UX: The entire workflow is completed within the chat interface, significantly reducing traditional game development's communication and operational barriers. Users accomplish complex game design and development through conversation while receiving real-time feedback and adjustment suggestions, democratizing game creation. 6/ Industry-Disrupting Value: Transforms traditional manual development into AI-driven automated pipelines.

Kingnet AI

46,407 görüntüleme • 1 yıl önce

Higher deuterium in drinking water correlates with higher rates of major depression. Higher deuterium in drinking water correlates with higher rates of type 2 diabetes. This isn't a fringe hypothesis. It's a University of Utah population study mapping deuterium concentration in US tap water against disease prevalence across the country. Correlation — not proof of causation. But striking enough that Tatyana Strekalova — Clinician Scientist at Maastricht University, Senior Researcher at the University of Oxford, and Professor of Physiology at Sechenov First Moscow State Medical University — decided to test it in mice. The logic is straightforward. Deuterium concentration in drinking water decreases with distance from the ocean — inland, high-altitude water is naturally more deuterium-depleted. If geography determines deuterium load, and deuterium load correlates with disease prevalence, then what happens when you deliberately give animals water at the low end of that natural range? What her lab found is striking. They used 90 ppm deuterium-depleted water throughout — the lowest deuterium concentration found naturally in drinking water on Earth. Not a pharmaceutical dose. Not exotic laboratory depletion. And compared it to control mice drinking 140 ppm water. The difference between antarctic meltwater and coastal tap water. Finding 1: Aged mice. Two weeks. Depressive-like behaviors measurably reduced They took 18-month-old mice — equivalent to very old age, approaching maximum mouse lifespan. These mice displayed measurable depressive behaviors: Anhedonia — loss of sensitivity to reward, measured by reduced preference for sweet water. Increased helplessness behavior. Reduced novelty exploration. Impaired hippocampus-dependent memory. After two weeks on 90 ppm DDW? Sucrose preference increased — anhedonia reversed. Helplessness behavior significantly reduced. Novelty exploration improved. Hippocampal memory improved. Two weeks. Naturally occurring low-deuterium water. Measurable reversal across four independent behavioral markers. Finding 2: DDW matched antidepressant effect in stressed young mice They used a chronic stress model in young mice — predator scent, restraint stress, tail suspension. This reliably induces anhedonia in susceptible mice. Then they divided mice into three groups: normal water at 140 ppm, DDW at 90 ppm, and citalopram — a standard SSRI antidepressant. DDW produced rescue of sucrose preference comparable to citalopram. Helplessness behavior was also rescued comparably. Serotonin transporter expression — one of the key molecular targets of SSRIs — was rescued by both citalopram and DDW. This is mouse data. Direct translation to humans requires clinical trials. But the mechanism convergence is a legitimate finding. Finding 3: DDW normalized REM sleep in stressed mice Depression is the only psychiatric disorder diagnosable by a specific sleep architecture change — increased REM sleep. It's a biological marker, not a subjective report. Stressed mice showed increased REM sleep — the biological depression signature. DDW normalized REM sleep. Slow-wave sleep and wakefulness also improved. Finding 4: DDW protected against western diet-induced cognitive impairment and glucose dysregulation They used 12-month-old female mice on a standardized western diet — high saturated fat, high sugar, high cholesterol. Western diet produced: impaired glucose tolerance, brain inflammation, reduced mitochondrial markers in brain and liver, liver steatosis — fat accumulation in the liver — impaired object recognition memory, and impaired hippocampal memory. DDW at 90 ppm on western diet: Prevented glucose tolerance impairment despite continued western diet. Improved object recognition memory. Improved hippocampal memory in old mice. Did not improve liver steatosis. The liver finding matters. DDW protected the brain and metabolic glucose handling but did not reverse the liver damage. Strekalova’s interpretation: DDW is counteracting brain inflammation driven by damage elsewhere in the body — not fixing the damage itself. Finding 5: Deuterium-enriched water (180 ppm) did the opposite They tested the reverse — 180 ppm water, equivalent to what you would find in evaporative pools in the Sahara. Results on aged mice with western diet: Novelty exploration clearly suppressed. Hippocampus-dependent memory suppressed. Strekalova describes this as a surprise finding. 90 ppm sits at the low end of natural drinking water on Earth. 180 ppm sits at the high end of what occurs in extreme arid environments. The difference between them produced measurable opposing effects on memory and cognition in aged mice. Gene expression — deuterium sits upstream of circadian biology In both the aging model and the stress model, DDW altered gene expression in the hippocampus and prefrontal cortex. Affected categories: DNA repair, oxidative stress response, immune regulation, mitochondrial function, cellular plasticity, aging-related genes. And one finding that connects directly to everything else: Per2. Per2 is a core circadian clock gene. DDW affecting its expression means deuterium content influences circadian biology at the gene level. Deuterium doesn't just affect cancer cells and mitochondrial efficiency. It sits upstream of the circadian system. The implication The deuterium content of your water varies by geography. Antarctic water versus coastal tap water. The difference is measurable. The biological effects in these models are measurable and opposing. Most people optimizing their health are tracking sleep, sunlight, training, and nutrition. Nobody told them deuterium was also on the list.
9:09

Higher deuterium in drinking water correlates with higher rates of major depression. Higher deuterium in drinking water correlates with higher rates of type 2 diabetes. This isn't a fringe hypothesis. It's a University of Utah population study mapping deuterium concentration in US tap water against disease prevalence across the country. Correlation — not proof of causation. But striking enough that Tatyana Strekalova — Clinician Scientist at Maastricht University, Senior Researcher at the University of Oxford, and Professor of Physiology at Sechenov First Moscow State Medical University — decided to test it in mice. The logic is straightforward. Deuterium concentration in drinking water decreases with distance from the ocean — inland, high-altitude water is naturally more deuterium-depleted. If geography determines deuterium load, and deuterium load correlates with disease prevalence, then what happens when you deliberately give animals water at the low end of that natural range? What her lab found is striking. They used 90 ppm deuterium-depleted water throughout — the lowest deuterium concentration found naturally in drinking water on Earth. Not a pharmaceutical dose. Not exotic laboratory depletion. And compared it to control mice drinking 140 ppm water. The difference between antarctic meltwater and coastal tap water. Finding 1: Aged mice. Two weeks. Depressive-like behaviors measurably reduced They took 18-month-old mice — equivalent to very old age, approaching maximum mouse lifespan. These mice displayed measurable depressive behaviors: Anhedonia — loss of sensitivity to reward, measured by reduced preference for sweet water. Increased helplessness behavior. Reduced novelty exploration. Impaired hippocampus-dependent memory. After two weeks on 90 ppm DDW? Sucrose preference increased — anhedonia reversed. Helplessness behavior significantly reduced. Novelty exploration improved. Hippocampal memory improved. Two weeks. Naturally occurring low-deuterium water. Measurable reversal across four independent behavioral markers. Finding 2: DDW matched antidepressant effect in stressed young mice They used a chronic stress model in young mice — predator scent, restraint stress, tail suspension. This reliably induces anhedonia in susceptible mice. Then they divided mice into three groups: normal water at 140 ppm, DDW at 90 ppm, and citalopram — a standard SSRI antidepressant. DDW produced rescue of sucrose preference comparable to citalopram. Helplessness behavior was also rescued comparably. Serotonin transporter expression — one of the key molecular targets of SSRIs — was rescued by both citalopram and DDW. This is mouse data. Direct translation to humans requires clinical trials. But the mechanism convergence is a legitimate finding. Finding 3: DDW normalized REM sleep in stressed mice Depression is the only psychiatric disorder diagnosable by a specific sleep architecture change — increased REM sleep. It's a biological marker, not a subjective report. Stressed mice showed increased REM sleep — the biological depression signature. DDW normalized REM sleep. Slow-wave sleep and wakefulness also improved. Finding 4: DDW protected against western diet-induced cognitive impairment and glucose dysregulation They used 12-month-old female mice on a standardized western diet — high saturated fat, high sugar, high cholesterol. Western diet produced: impaired glucose tolerance, brain inflammation, reduced mitochondrial markers in brain and liver, liver steatosis — fat accumulation in the liver — impaired object recognition memory, and impaired hippocampal memory. DDW at 90 ppm on western diet: Prevented glucose tolerance impairment despite continued western diet. Improved object recognition memory. Improved hippocampal memory in old mice. Did not improve liver steatosis. The liver finding matters. DDW protected the brain and metabolic glucose handling but did not reverse the liver damage. Strekalova’s interpretation: DDW is counteracting brain inflammation driven by damage elsewhere in the body — not fixing the damage itself. Finding 5: Deuterium-enriched water (180 ppm) did the opposite They tested the reverse — 180 ppm water, equivalent to what you would find in evaporative pools in the Sahara. Results on aged mice with western diet: Novelty exploration clearly suppressed. Hippocampus-dependent memory suppressed. Strekalova describes this as a surprise finding. 90 ppm sits at the low end of natural drinking water on Earth. 180 ppm sits at the high end of what occurs in extreme arid environments. The difference between them produced measurable opposing effects on memory and cognition in aged mice. Gene expression — deuterium sits upstream of circadian biology In both the aging model and the stress model, DDW altered gene expression in the hippocampus and prefrontal cortex. Affected categories: DNA repair, oxidative stress response, immune regulation, mitochondrial function, cellular plasticity, aging-related genes. And one finding that connects directly to everything else: Per2. Per2 is a core circadian clock gene. DDW affecting its expression means deuterium content influences circadian biology at the gene level. Deuterium doesn't just affect cancer cells and mitochondrial efficiency. It sits upstream of the circadian system. The implication The deuterium content of your water varies by geography. Antarctic water versus coastal tap water. The difference is measurable. The biological effects in these models are measurable and opposing. Most people optimizing their health are tracking sleep, sunlight, training, and nutrition. Nobody told them deuterium was also on the list.

no.mind

17,344 görüntüleme • 2 ay önce

21 days that rewired my life. 🌿 Imagine taking a leap of faith - stepping away from the chaos of modern life into an Ayurvedic sanctuary called Vaidyagrama, nestled in Coimbatore's outskirts. I learned about this place from Rahul Dewan (thanks to my good friend Rakesh P Sheth for the connection). Unplugging from work @teaboxteaTea and surrendering my phone? Terrifying. But sometimes the best gifts come disguised as challenges. 😅 In the heart of nature, this 17-year-old healing haven (founded by Dr. Ram Kumar and his classmates) quietly transforms lives. I later learned they've treated foreign presidents and politicians, yet maintained their humble roots throughout. The daily rhythm was beautifully structured: 6 AM medicines from caring akkas, herbal tea at 7:30, breakfast around 8. Treatments took only 1-2 hours, leaving plenty of time for reflection and rest. Afternoon herbal tea at 4, and dinner early at 6. The highlight? Satsangs with Dr. Ram Kumar - enlightening discussions on Ayurveda, health, and wellness. We experienced sound healing sessions, witnessed Kalari Pattu demonstrations, and joined daily poojas. 🧘‍♂️ The peaceful surroundings made everything special - watching peacocks stroll by at dawn while sipping herbal tea, finding moments of pure stillness. 🦚 Won't detail my panchakarma treatment (definitely not for the faint-hearted 😅), but the results? Mind-blowing: Stubborn 2-year eczema-reduced by 70% (where regular treatments failed) Looking 7-10 years younger (CitiBank app couldn't recognize my face!😅) Eyesight improved by 0.5 (needed new glasses!) Most humbling were my fellow healers - a 4th stage brain cancer survivor from HK/US, people reversing diabetes and significantly improving Alzheimer's and other serious conditions. Many on their 2nd-8th visit, finding hope and results where modern medicine had reached its limits. 21 days. That's all it took to remind me that sometimes, ancient wisdom holds answers our modern world is still searching for. Forever grateful for this reset button on life. 🙏 P.S. Feel free to reach out if you'd like to know more about this journey.
0:31

21 days that rewired my life. 🌿 Imagine taking a leap of faith - stepping away from the chaos of modern life into an Ayurvedic sanctuary called Vaidyagrama, nestled in Coimbatore's outskirts. I learned about this place from Rahul Dewan (thanks to my good friend Rakesh P Sheth for the connection). Unplugging from work @teaboxteaTea and surrendering my phone? Terrifying. But sometimes the best gifts come disguised as challenges. 😅 In the heart of nature, this 17-year-old healing haven (founded by Dr. Ram Kumar and his classmates) quietly transforms lives. I later learned they've treated foreign presidents and politicians, yet maintained their humble roots throughout. The daily rhythm was beautifully structured: 6 AM medicines from caring akkas, herbal tea at 7:30, breakfast around 8. Treatments took only 1-2 hours, leaving plenty of time for reflection and rest. Afternoon herbal tea at 4, and dinner early at 6. The highlight? Satsangs with Dr. Ram Kumar - enlightening discussions on Ayurveda, health, and wellness. We experienced sound healing sessions, witnessed Kalari Pattu demonstrations, and joined daily poojas. 🧘‍♂️ The peaceful surroundings made everything special - watching peacocks stroll by at dawn while sipping herbal tea, finding moments of pure stillness. 🦚 Won't detail my panchakarma treatment (definitely not for the faint-hearted 😅), but the results? Mind-blowing: Stubborn 2-year eczema-reduced by 70% (where regular treatments failed) Looking 7-10 years younger (CitiBank app couldn't recognize my face!😅) Eyesight improved by 0.5 (needed new glasses!) Most humbling were my fellow healers - a 4th stage brain cancer survivor from HK/US, people reversing diabetes and significantly improving Alzheimer's and other serious conditions. Many on their 2nd-8th visit, finding hope and results where modern medicine had reached its limits. 21 days. That's all it took to remind me that sometimes, ancient wisdom holds answers our modern world is still searching for. Forever grateful for this reset button on life. 🙏 P.S. Feel free to reach out if you'd like to know more about this journey.

Kausshal | Teapreneur

178,841 görüntüleme • 1 yıl önce

Vera-Ellen’s Unmatched “Nerve Taps”: The Technique No One Could Copy In the 1945 film Wonder Man, Vera-Ellen delivers a dazzling display of what is often called “nerve taps”—a rapid-fire footwork style that the post describes as her unique “neural touch.” This was no ordinary tap dancing. By keeping her feet extremely close to the floor and moving them at lightning speed, she produced a continuous, machine-gun-like cascade of sound and motion. The result was a signature technique so precise and demanding that it has rarely, if ever, been fully replicated on screen. The essence of the technique lies in minimizing the distance between the foot and the floor. Instead of lifting high and then striking, Vera-Ellen reduced the travel path of each step to the absolute minimum. This allowed her to fit far more taps into every second while maintaining clarity and power. Achieving that speed required exceptional strength and flexibility in the ankles and the small muscles of the feet and lower legs. The movement often looked less like separate steps and more like a controlled vibration—tight, rapid, and relentless. What made the performance even more remarkable was the contrast between her lower and upper body. While her feet worked at an almost impossible pace, her torso remained largely still and elegant. Shoulders and arms moved with subtle independence, yet the core stayed calm and balanced. This separation of body parts demanded a level of muscular control usually associated with classical ballet. Vera-Ellen further raised the difficulty by incorporating elements of pointe work without the support of traditional pointe shoes, adding another layer of precision and risk. Her uniqueness stemmed from a rare combination of training and physical gifts. She began with rigorous ballet study, later became one of the youngest Rockettes, and then expanded into tap, jazz, and other forms. That broad foundation allowed her to bring balletic lightness and upper-body composure into the high-speed world of tap. Many dancers can move quickly, but few can sustain such velocity while keeping every tap clean, even, and musically precise. Vera-Ellen made it appear almost effortless. She also approached the floor with a kind of mechanical efficiency. By staying low and close to the ground, she maximized speed without relying on exaggerated force or large, showy movements. The result was power delivered through refinement rather than sheer muscular display—strong yet graceful, intense yet controlled. More than eighty years later, the technique still stands out as a personal signature. The combination of extreme speed, minimal elevation, independent body control, and balletic poise created a barrier that later dancers have found nearly impossible to cross completely. Vera-Ellen’s nerve taps were not merely a skill; they were a physical language uniquely her own, a lasting reminder of one of Hollywood’s most extraordinary and under-celebrated dance talents.
1:26

Vera-Ellen’s Unmatched “Nerve Taps”: The Technique No One Could Copy In the 1945 film Wonder Man, Vera-Ellen delivers a dazzling display of what is often called “nerve taps”—a rapid-fire footwork style that the post describes as her unique “neural touch.” This was no ordinary tap dancing. By keeping her feet extremely close to the floor and moving them at lightning speed, she produced a continuous, machine-gun-like cascade of sound and motion. The result was a signature technique so precise and demanding that it has rarely, if ever, been fully replicated on screen. The essence of the technique lies in minimizing the distance between the foot and the floor. Instead of lifting high and then striking, Vera-Ellen reduced the travel path of each step to the absolute minimum. This allowed her to fit far more taps into every second while maintaining clarity and power. Achieving that speed required exceptional strength and flexibility in the ankles and the small muscles of the feet and lower legs. The movement often looked less like separate steps and more like a controlled vibration—tight, rapid, and relentless. What made the performance even more remarkable was the contrast between her lower and upper body. While her feet worked at an almost impossible pace, her torso remained largely still and elegant. Shoulders and arms moved with subtle independence, yet the core stayed calm and balanced. This separation of body parts demanded a level of muscular control usually associated with classical ballet. Vera-Ellen further raised the difficulty by incorporating elements of pointe work without the support of traditional pointe shoes, adding another layer of precision and risk. Her uniqueness stemmed from a rare combination of training and physical gifts. She began with rigorous ballet study, later became one of the youngest Rockettes, and then expanded into tap, jazz, and other forms. That broad foundation allowed her to bring balletic lightness and upper-body composure into the high-speed world of tap. Many dancers can move quickly, but few can sustain such velocity while keeping every tap clean, even, and musically precise. Vera-Ellen made it appear almost effortless. She also approached the floor with a kind of mechanical efficiency. By staying low and close to the ground, she maximized speed without relying on exaggerated force or large, showy movements. The result was power delivered through refinement rather than sheer muscular display—strong yet graceful, intense yet controlled. More than eighty years later, the technique still stands out as a personal signature. The combination of extreme speed, minimal elevation, independent body control, and balletic poise created a barrier that later dancers have found nearly impossible to cross completely. Vera-Ellen’s nerve taps were not merely a skill; they were a physical language uniquely her own, a lasting reminder of one of Hollywood’s most extraordinary and under-celebrated dance talents.

Emotion & Music

16,749 görüntüleme • 25 gün önce

POV: You came to watch the World Cup... and ended up stealing the spotlight. ⚽ A stylish Good Morning from New York ends with a Messi-inspired dribble under the World Cup spotlight. Which nation are you representing at World Cup 2026? Create your own World Cup story with SeaArt.Ai🐋 , earn free credits, and get a chance to win an iPhone 17 Made with Seedance. SeaArt Creator Lab Prompt: World Cup 2026 Match-Day Fashion Story — Argentina Theme (0–25 Seconds) | New York | 16:9 Cinematic Ultra-realistic cinematic sports-fashion commercial, 16:9 horizontal, World Cup 2026 atmosphere, New York City. A beautiful, stylish American woman with a naturally elegant appearance, healthy glowing skin, expressive eyes, sleek updo hairstyle with soft face-framing strands. Premium commercial quality, realistic lighting, stable identity, natural movement, no glitches. 0–4 Seconds Luxury suite in a high-rise Manhattan hotel overlooking the New York skyline. Afternoon sunlight streams through floor-to-ceiling windows. The woman stands in front of an open wardrobe containing several football jerseys. She wears a fitted white tank top and black tailored shorts. Camera slowly pushes in as she scans the jerseys with anticipation. 4–7 Seconds Quick cinematic whip-pan transition. Her hand reaches for an Argentina jersey. The sky-blue and white stripes catch the sunlight. Realistic cloth physics. The jersey unfolds elegantly as she lifts it from the wardrobe. Subtle lens flare and premium fashion-commercial lighting. 7–11 Seconds Fast-paced transformation montage synchronized with energetic transitions. She ties the Argentina jersey into a stylish knot, puts on oversized light-wash baggy jeans and clean white sneakers. She grabs a sleek luxury-style crossbody bag with no visible branding. Mirror shots. She adjusts her hair. Flawless influencer-style makeup. Dynamic reflections and realistic daylight behavior. 11–15 Seconds Match cut. She walks confidently through a luxury Manhattan hotel corridor. Polished marble floors, warm ambient lighting, premium fashion-week atmosphere. A handsome European actor-type celebrity figure walks past in the opposite direction. They exchange a brief smile while continuing naturally. Cinematic depth of field, realistic background movement. 15–19 Seconds Hotel doors open dramatically. Bright New York sunlight floods the frame. Fast-paced tracking shots through Manhattan streets. Yellow taxis pass in the background. Crowds move naturally. Wind reacts realistically with her hair and jersey fabric. Low-angle walking shots. Premium sports-commercial energy. 19–22 Seconds Cut to a massive World Cup 2026 stadium atmosphere in New York. Argentina supporters fill the area. Sky-blue and white flags wave everywhere. Stadium lights begin illuminating as late afternoon shifts toward evening. The crowd energy grows stronger. The sound of anticipation fills the environment. 22–25 Seconds She enters the main stadium plaza. A football rolls across her path. She smiles playfully and begins a smooth, Messi-inspired close-control dribble sequence while moving forward. Tiny touches, effortless control, subtle body feints, the ball remaining close to her feet. Argentina fans around her cheer and react. Camera drops to a low tracking angle following the ball and her feet as she glides through the crowd. The sequence ends with her stopping the ball softly beneath her foot, looking toward the glowing stadium interior with excitement and confidence as Argentina colors illuminate the background. Style: ultra-realistic sports commercial, World Cup 2026 energy, premium color grading, realistic skin texture, cinematic depth of field, natural crowd behavior, subtle film grain, luxury fashion aesthetic, stable identity, high-end broadcast quality. Negative Prompt: blurry face, identity drift, duplicate people, extra limbs, distorted hands, unrealistic football movement, cartoon style, oversaturated colors, text overlays, logos, flickering artifacts, low resolution, unnatural physics, motion glitches, warped body proportions. #WorldCup2026 #SeaArtWorldCup
0:25

POV: You came to watch the World Cup... and ended up stealing the spotlight. ⚽ A stylish Good Morning from New York ends with a Messi-inspired dribble under the World Cup spotlight. Which nation are you representing at World Cup 2026? Create your own World Cup story with SeaArt.Ai🐋 , earn free credits, and get a chance to win an iPhone 17 Made with Seedance. SeaArt Creator Lab Prompt: World Cup 2026 Match-Day Fashion Story — Argentina Theme (0–25 Seconds) | New York | 16:9 Cinematic Ultra-realistic cinematic sports-fashion commercial, 16:9 horizontal, World Cup 2026 atmosphere, New York City. A beautiful, stylish American woman with a naturally elegant appearance, healthy glowing skin, expressive eyes, sleek updo hairstyle with soft face-framing strands. Premium commercial quality, realistic lighting, stable identity, natural movement, no glitches. 0–4 Seconds Luxury suite in a high-rise Manhattan hotel overlooking the New York skyline. Afternoon sunlight streams through floor-to-ceiling windows. The woman stands in front of an open wardrobe containing several football jerseys. She wears a fitted white tank top and black tailored shorts. Camera slowly pushes in as she scans the jerseys with anticipation. 4–7 Seconds Quick cinematic whip-pan transition. Her hand reaches for an Argentina jersey. The sky-blue and white stripes catch the sunlight. Realistic cloth physics. The jersey unfolds elegantly as she lifts it from the wardrobe. Subtle lens flare and premium fashion-commercial lighting. 7–11 Seconds Fast-paced transformation montage synchronized with energetic transitions. She ties the Argentina jersey into a stylish knot, puts on oversized light-wash baggy jeans and clean white sneakers. She grabs a sleek luxury-style crossbody bag with no visible branding. Mirror shots. She adjusts her hair. Flawless influencer-style makeup. Dynamic reflections and realistic daylight behavior. 11–15 Seconds Match cut. She walks confidently through a luxury Manhattan hotel corridor. Polished marble floors, warm ambient lighting, premium fashion-week atmosphere. A handsome European actor-type celebrity figure walks past in the opposite direction. They exchange a brief smile while continuing naturally. Cinematic depth of field, realistic background movement. 15–19 Seconds Hotel doors open dramatically. Bright New York sunlight floods the frame. Fast-paced tracking shots through Manhattan streets. Yellow taxis pass in the background. Crowds move naturally. Wind reacts realistically with her hair and jersey fabric. Low-angle walking shots. Premium sports-commercial energy. 19–22 Seconds Cut to a massive World Cup 2026 stadium atmosphere in New York. Argentina supporters fill the area. Sky-blue and white flags wave everywhere. Stadium lights begin illuminating as late afternoon shifts toward evening. The crowd energy grows stronger. The sound of anticipation fills the environment. 22–25 Seconds She enters the main stadium plaza. A football rolls across her path. She smiles playfully and begins a smooth, Messi-inspired close-control dribble sequence while moving forward. Tiny touches, effortless control, subtle body feints, the ball remaining close to her feet. Argentina fans around her cheer and react. Camera drops to a low tracking angle following the ball and her feet as she glides through the crowd. The sequence ends with her stopping the ball softly beneath her foot, looking toward the glowing stadium interior with excitement and confidence as Argentina colors illuminate the background. Style: ultra-realistic sports commercial, World Cup 2026 energy, premium color grading, realistic skin texture, cinematic depth of field, natural crowd behavior, subtle film grain, luxury fashion aesthetic, stable identity, high-end broadcast quality. Negative Prompt: blurry face, identity drift, duplicate people, extra limbs, distorted hands, unrealistic football movement, cartoon style, oversaturated colors, text overlays, logos, flickering artifacts, low resolution, unnatural physics, motion glitches, warped body proportions. #WorldCup2026 #SeaArtWorldCup

Shami

39,301 görüntüleme • 2 ay önce

Created with seedance 2.5 Use Image as the only identity reference for the adult Korean female protagonist. Preserve her exact face, proportions, skin tone, hairstyle, and body proportions throughout. No character name. Setting: Old Korean school sports complex at night—badminton court, wooden floor, frosted windows, equipment racks, mats, benches, sprinkler pipes, fluorescent/emergency lighting. A damaged sprinkler floods the floor. Protagonist wardrobe: charcoal athletic jacket, muted-blue shirt, black training pants, worn white-gray sneakers, dark wristband. Photorealistic skin, pores, sweat, hair, breathing. TIMELINE — 30 SECONDS 00:00–00:05 — Trapped She backs through the empty gym as four adult female attackers surround her. She tries to escape, ducks an attack, and an attacker crashes into equipment. Handheld camera, realistic impacts. 00:05–00:09 — Awakening An attacker damages a sprinkler. Water spreads across the floor. She steps into it. Her breathing slows. Tiny ripples mysteriously move against the floor slope toward her foot. She notices, confused. 00:09–00:13 — First Control An attacker grabs her. She twists and uses reduced water friction to escape naturally. A thin water arc follows her foot. Another strike approaches; a transparent rotating layer of water redirects it. No beams or projectiles. 00:13–00:18 — Instinctive Control Two attackers rush. Her foot pushes through the water, creating a low wave that disrupts balance. She uses controlled physical strikes. Water briefly compresses around contact points, producing believable hydraulic force. 00:18–00:23 — Escalation Attackers surround her. She slides, pivots, ducks and redirects attacks using realistic biomechanics. Water follows her movements naturally. Physical contact always occurs before any water effect. 00:23–00:27 — Breakthrough Exhausted, she touches the flooded floor. Ripples from across the gym converge toward her. A shallow circular current forms around her feet. She accelerates forward naturally, lands several controlled physical strikes, and sends a final hydraulic burst through the surrounding water. 00:27–00:30 — Aftermath A realistic pressure wave spreads across the wet floor, sliding loose equipment. The attackers remain alive and stunned. She stands exhausted, water dripping from her hair and clothes. A few droplets briefly respond to her hand, then fall. She walks toward the exit. Emergency lights flicker. CUT TO BLACK at exactly 30 seconds. STYLE & PHYSICS Photorealistic live-action supernatural action. Water obeys gravity, inertia, surface tension, friction, pressure, splashing, reflections, and realistic fluid dynamics. She can only manipulate existing water—never create it, solidify it, form creatures, shoot beams, fly, or teleport. Every combat effect follows: visible target → body movement → physical contact → natural reaction → water response. CAMERA & AUDIO Reactive handheld cinematography: shoulder tracking, close pursuit, low floor shots, whip-pans, focus pulls, impact shake, occasional camera lag. No impossible movement or glossy commercial shots. Native sound only: fluorescent hum, rain, footsteps, breathing, clothing, impacts, metal vibration, sprinkler hiss, water splashes and gym echo. No music, dialogue, subtitles, logos, or watermark. Strict negative: face drift, duplicated protagonist, wardrobe/hairstyle changes, copied choreography, glowing aura, lasers, magic circles, giant waves, teleportation, flying, anime effects, CGI humans, plastic skin, gore, broken anatomy, impossible physics, excessive slow motion, or superhero poses.
0:30

Created with seedance 2.5 Use Image as the only identity reference for the adult Korean female protagonist. Preserve her exact face, proportions, skin tone, hairstyle, and body proportions throughout. No character name. Setting: Old Korean school sports complex at night—badminton court, wooden floor, frosted windows, equipment racks, mats, benches, sprinkler pipes, fluorescent/emergency lighting. A damaged sprinkler floods the floor. Protagonist wardrobe: charcoal athletic jacket, muted-blue shirt, black training pants, worn white-gray sneakers, dark wristband. Photorealistic skin, pores, sweat, hair, breathing. TIMELINE — 30 SECONDS 00:00–00:05 — Trapped She backs through the empty gym as four adult female attackers surround her. She tries to escape, ducks an attack, and an attacker crashes into equipment. Handheld camera, realistic impacts. 00:05–00:09 — Awakening An attacker damages a sprinkler. Water spreads across the floor. She steps into it. Her breathing slows. Tiny ripples mysteriously move against the floor slope toward her foot. She notices, confused. 00:09–00:13 — First Control An attacker grabs her. She twists and uses reduced water friction to escape naturally. A thin water arc follows her foot. Another strike approaches; a transparent rotating layer of water redirects it. No beams or projectiles. 00:13–00:18 — Instinctive Control Two attackers rush. Her foot pushes through the water, creating a low wave that disrupts balance. She uses controlled physical strikes. Water briefly compresses around contact points, producing believable hydraulic force. 00:18–00:23 — Escalation Attackers surround her. She slides, pivots, ducks and redirects attacks using realistic biomechanics. Water follows her movements naturally. Physical contact always occurs before any water effect. 00:23–00:27 — Breakthrough Exhausted, she touches the flooded floor. Ripples from across the gym converge toward her. A shallow circular current forms around her feet. She accelerates forward naturally, lands several controlled physical strikes, and sends a final hydraulic burst through the surrounding water. 00:27–00:30 — Aftermath A realistic pressure wave spreads across the wet floor, sliding loose equipment. The attackers remain alive and stunned. She stands exhausted, water dripping from her hair and clothes. A few droplets briefly respond to her hand, then fall. She walks toward the exit. Emergency lights flicker. CUT TO BLACK at exactly 30 seconds. STYLE & PHYSICS Photorealistic live-action supernatural action. Water obeys gravity, inertia, surface tension, friction, pressure, splashing, reflections, and realistic fluid dynamics. She can only manipulate existing water—never create it, solidify it, form creatures, shoot beams, fly, or teleport. Every combat effect follows: visible target → body movement → physical contact → natural reaction → water response. CAMERA & AUDIO Reactive handheld cinematography: shoulder tracking, close pursuit, low floor shots, whip-pans, focus pulls, impact shake, occasional camera lag. No impossible movement or glossy commercial shots. Native sound only: fluorescent hum, rain, footsteps, breathing, clothing, impacts, metal vibration, sprinkler hiss, water splashes and gym echo. No music, dialogue, subtitles, logos, or watermark. Strict negative: face drift, duplicated protagonist, wardrobe/hairstyle changes, copied choreography, glowing aura, lasers, magic circles, giant waves, teleportation, flying, anime effects, CGI humans, plastic skin, gore, broken anatomy, impossible physics, excessive slow motion, or superhero poses.

Anissa

18,511 görüntüleme • 15 gün önce

Seedance 2.5 full 30s prompt + H3 lipsync workflow are below 👇 [3_panel_ref] is a three-panel character turnaround (front, left profile, rear) that strictly defines one single continuous Black woman: short black hair with a sharp undercut on one side, large gold hoop earrings, thick gold curb-chain necklace, fitted black short-sleeve crop top exposing the midriff, baggy green woodland-camouflage cargo pants with large side pockets, and black-and-white Nike high-top sneakers with leopard-print heel and tongue panels. Use only her exact face, body proportions, hair, clothing and accessories. Do not use the plain studio background or any lighting from this image. [outdoor_ref] provides the outdoor lakeside music festival environment at blue-hour dusk: dense standing crowd, main stage with large LED screens and vibrant purple-cyan-pink lighting, scattered white and colored tents, flags, gravel paths, dark forested hills, and calm reflective lake water. Use this only for location layout, spatial depth, ambient lighting mix (cool dusk sky + warm practical tent lights + stage color spill), and background crowd density. Do not promote any specific individuals from this image into the main subject role. Generate a continuous 30-second photorealistic cinematic sequence of the exact same woman from [3_panel_ref] advancing through the dense festival crowd of [outdoor_ref] while carrying a clear plastic cup of beer in each hand. Her path is a single continuous forward trajectory from deep inside the packed crowd toward a slightly clearer edge near the water where her friends wait. All camera work and cuts preserve the unbroken sense of her physical progress and direction of travel. No dialogue, no subtitles, no on-screen text. 0-3s: Hybrid SnorriCam + Steadicam locked on her face and upper torso. She pushes and weaves through tightly packed bodies, constantly bumped from left, right and behind. Both beer cups tilt and liquid sloshes; her expression cycles between tight frustration and stubborn determination. She never stops advancing. Stage lights and crowd motion blur behind her. 3.0-4.5s: Medium side tracking shot. A tall man shoulders into her hard — brief slow-motion impact, beer liquid arcs upward, then hard speed-ramp recovery as she re-centers and keeps moving forward. 4.5-6.0s: Extreme close-up on her right hand clamped around the plastic cup. Foam and golden liquid sway violently with each jolt; knuckles and gold chain details sharp. 6.0-7.5s: Low-angle close-up of her Nike sneakers with leopard accents stepping carefully between other people’s feet, discarded cups and uneven grass, still pushing ahead. 7.5-9.0s: Tight facial close-up with rapid zoom-in to her eyes and the swinging gold hoop earring. She glances sideways at a near-miss, jaw tight, then locks forward again. 9.0-10.5s: Medium three-quarter shot. She ducks under a raised dancing arm, speed-ramp on the duck and straighten, beers held high, continuing the same forward line. 10.5-12.0s: Slightly elevated back three-quarter. A surge of people passes; her crop top and gold chain catch stage light as she twists her torso to slip through without stopping. 12.0-13.5s: Extreme close-up on the left beer cup as it is jolted — liquid nearly overflows, catching a purple stage flash, then settles as she stabilizes. 13.5-15.0s: Dutch-angle medium shot. She brushes past a person with a large backpack; camo cargo pockets scrape fabric; her stride never breaks. 15.0-16.5s: Close-up on the undercut side of her head and short black hair moving with the motion, gold earring flashing. 16.5-18.0s: Slightly high angle looking down as she navigates a tighter cluster near tents; bodies part just enough for her to thread through, still advancing. 18.0-19.5s: Slow-motion medium close-up of her upper body and face as a drift of stage fog and colored light crosses her; skin, gold jewelry and black fabric catch the colors while she keeps walking. 19.5-21.0s: Side tracking speed-ramp. She sidesteps a couple holding hands, beers lifted protectively, path still straight toward the water edge. 21.0-22.5s: Extreme close-up on her slightly parted lips and focused eyes looking ahead, concentration unbroken. 22.5-24.0s: Medium full-body as the crowd density begins to thin near the lake. She picks up a half-step of speed while still carefully balancing both cups. 24.0-25.5s: Tracking from behind as she emerges into a small clearer zone. Two female friends in casual festival clothes are waiting a few meters ahead, already noticing her. 25.5-27.0s: Medium shot as she reaches them, stops, still holding both beers upright. Shoulders begin to drop. 27.0-30s: Close-up on her face. She releases a long, deep sigh of pure relief, a small tired smile appears, eyes soften toward her friends. Soft bokeh of lake water and residual stage lights behind her. Hold the final frame. Visual treatment: photorealistic live-action, natural blue-hour mixed with vibrant stage color spill, shallow depth of field on all close-ups, grounded handheld energy that never loses her continuous forward path. Audio: dense overlapping crowd murmur and chatter, distant muffled bass and music from the main stage, liquid sloshing inside the cups, soft body impacts and fabric rustle, her slightly elevated breathing, final clear relieved exhalation. No added music bed beyond the distant stage sound. Keep the woman’s identity, clothing, cup ownership and direction of travel completely stable across every cut. If you need the prompts for the references used, check the quoted post! 🫡
0:30

Seedance 2.5 full 30s prompt + H3 lipsync workflow are below 👇 [3_panel_ref] is a three-panel character turnaround (front, left profile, rear) that strictly defines one single continuous Black woman: short black hair with a sharp undercut on one side, large gold hoop earrings, thick gold curb-chain necklace, fitted black short-sleeve crop top exposing the midriff, baggy green woodland-camouflage cargo pants with large side pockets, and black-and-white Nike high-top sneakers with leopard-print heel and tongue panels. Use only her exact face, body proportions, hair, clothing and accessories. Do not use the plain studio background or any lighting from this image. [outdoor_ref] provides the outdoor lakeside music festival environment at blue-hour dusk: dense standing crowd, main stage with large LED screens and vibrant purple-cyan-pink lighting, scattered white and colored tents, flags, gravel paths, dark forested hills, and calm reflective lake water. Use this only for location layout, spatial depth, ambient lighting mix (cool dusk sky + warm practical tent lights + stage color spill), and background crowd density. Do not promote any specific individuals from this image into the main subject role. Generate a continuous 30-second photorealistic cinematic sequence of the exact same woman from [3_panel_ref] advancing through the dense festival crowd of [outdoor_ref] while carrying a clear plastic cup of beer in each hand. Her path is a single continuous forward trajectory from deep inside the packed crowd toward a slightly clearer edge near the water where her friends wait. All camera work and cuts preserve the unbroken sense of her physical progress and direction of travel. No dialogue, no subtitles, no on-screen text. 0-3s: Hybrid SnorriCam + Steadicam locked on her face and upper torso. She pushes and weaves through tightly packed bodies, constantly bumped from left, right and behind. Both beer cups tilt and liquid sloshes; her expression cycles between tight frustration and stubborn determination. She never stops advancing. Stage lights and crowd motion blur behind her. 3.0-4.5s: Medium side tracking shot. A tall man shoulders into her hard — brief slow-motion impact, beer liquid arcs upward, then hard speed-ramp recovery as she re-centers and keeps moving forward. 4.5-6.0s: Extreme close-up on her right hand clamped around the plastic cup. Foam and golden liquid sway violently with each jolt; knuckles and gold chain details sharp. 6.0-7.5s: Low-angle close-up of her Nike sneakers with leopard accents stepping carefully between other people’s feet, discarded cups and uneven grass, still pushing ahead. 7.5-9.0s: Tight facial close-up with rapid zoom-in to her eyes and the swinging gold hoop earring. She glances sideways at a near-miss, jaw tight, then locks forward again. 9.0-10.5s: Medium three-quarter shot. She ducks under a raised dancing arm, speed-ramp on the duck and straighten, beers held high, continuing the same forward line. 10.5-12.0s: Slightly elevated back three-quarter. A surge of people passes; her crop top and gold chain catch stage light as she twists her torso to slip through without stopping. 12.0-13.5s: Extreme close-up on the left beer cup as it is jolted — liquid nearly overflows, catching a purple stage flash, then settles as she stabilizes. 13.5-15.0s: Dutch-angle medium shot. She brushes past a person with a large backpack; camo cargo pockets scrape fabric; her stride never breaks. 15.0-16.5s: Close-up on the undercut side of her head and short black hair moving with the motion, gold earring flashing. 16.5-18.0s: Slightly high angle looking down as she navigates a tighter cluster near tents; bodies part just enough for her to thread through, still advancing. 18.0-19.5s: Slow-motion medium close-up of her upper body and face as a drift of stage fog and colored light crosses her; skin, gold jewelry and black fabric catch the colors while she keeps walking. 19.5-21.0s: Side tracking speed-ramp. She sidesteps a couple holding hands, beers lifted protectively, path still straight toward the water edge. 21.0-22.5s: Extreme close-up on her slightly parted lips and focused eyes looking ahead, concentration unbroken. 22.5-24.0s: Medium full-body as the crowd density begins to thin near the lake. She picks up a half-step of speed while still carefully balancing both cups. 24.0-25.5s: Tracking from behind as she emerges into a small clearer zone. Two female friends in casual festival clothes are waiting a few meters ahead, already noticing her. 25.5-27.0s: Medium shot as she reaches them, stops, still holding both beers upright. Shoulders begin to drop. 27.0-30s: Close-up on her face. She releases a long, deep sigh of pure relief, a small tired smile appears, eyes soften toward her friends. Soft bokeh of lake water and residual stage lights behind her. Hold the final frame. Visual treatment: photorealistic live-action, natural blue-hour mixed with vibrant stage color spill, shallow depth of field on all close-ups, grounded handheld energy that never loses her continuous forward path. Audio: dense overlapping crowd murmur and chatter, distant muffled bass and music from the main stage, liquid sloshing inside the cups, soft body impacts and fabric rustle, her slightly elevated breathing, final clear relieved exhalation. No added music bed beyond the distant stage sound. Keep the woman’s identity, clothing, cup ownership and direction of travel completely stable across every cut. If you need the prompts for the references used, check the quoted post! 🫡

TechHalla

14,854 görüntüleme • 28 gün önce

Exposing the Greatest Scammer On Earth: Mwai Kibaki’s Economic Betrayal of Kenya In the Kenyan economic landscape, intermediaries such as importers, exporters, and traders have played a central role in perpetuating a system of circulation without creation, turning the country into a dumping site for low-value imports and waste from abroad. This has enriched these groups while stifling local production and exacerbating inequality. Daniel arap Moi father of prolific scammer and thief Gideon K. Moi , contributed significantly during his tenure by implementing Structural Adjustment Programs (SAPs) backed by the World Bank Kenya , which created a free economic zone that allowed these intermediaries to exploit funding and market freedoms, leading to the erosion of domestic industries. The World Bank, through its funding programs, facilitated this shift, prioritizing external trade over internal growth and enabling the intermediaries to dominate without fostering genuine economic development. Colonial legacies have further entrenched this collapse, with institutions like KEPSA KENYA and KAM emerging as products of British colonial rule, where enterprise control was handed to mainly Asian companies and intermediaries who acted as brokers in a somewhat open market. These Asian companies capitalized on the post-colonial environment, monopolizing sectors and closing avenues for local production, especially under Moi’s policies and later escalations. The British colonial administration set the foundation by establishing these broker-dominated structures, ensuring that post-independence Kenya remained dependent on foreign-controlled trade networks, which deliberately suppressed indigenous manufacturing and innovation. Under Mwai Kibaki’s leadership from 2002 to 2013, the situation worsened as monopolies were solidified, making production costs prohibitively high through barriers like impossible licensing requirements and subcontracting practices. Government and state officials enriched themselves via contracts and policies that favored these intermediaries, using trade barriers to prevent local competition. Banks, as rich enterprises, profited immensely without producing anything tangible, circulating funds in a superficial economy. Similarly, insurance companies thrived amid a crumbling healthcare system, draining resources while supermarkets like Naivas Quickmart Kenya Carrefour Kenya Mathai Supermarkets and shops amassed profits despite widespread poverty, all by moving imported goods and erecting barriers to local alternatives. Lobby groups and corrupt bodies, such as the Kenya Bankers Association (KBA) KEPSA KENYA KAM AKI DLAK , have actively contributed by outsourcing production abroad - often to China - and posing as local producers, killing skills training for workers and turning factories into mere distribution hubs. These white-collar fraudsters influence policy by bribing MPigs in parliament’s toilets, to maintain their dominance, collaborating with religious leaders like Archbishop Anthony Muheria Jackson Ole Sapit Anglican Church of Kenya KENYA CONFERENCE OF CATHOLIC BISHOPS(KCCB) who are bribed to agitate against local products like traditional alcohol, promoting bans that suppress innovation and responsible regulation. This religious overlay, rooted in colonial hangovers, stifles rational approaches to issues like HIV prevention and economic self-reliance, ensuring that elite brokers continue harvesting wealth while the broader Kenyan economy remains in survival mode, starved of oxygen for growth.
9:59

Exposing the Greatest Scammer On Earth: Mwai Kibaki’s Economic Betrayal of Kenya In the Kenyan economic landscape, intermediaries such as importers, exporters, and traders have played a central role in perpetuating a system of circulation without creation, turning the country into a dumping site for low-value imports and waste from abroad. This has enriched these groups while stifling local production and exacerbating inequality. Daniel arap Moi father of prolific scammer and thief Gideon K. Moi , contributed significantly during his tenure by implementing Structural Adjustment Programs (SAPs) backed by the World Bank Kenya , which created a free economic zone that allowed these intermediaries to exploit funding and market freedoms, leading to the erosion of domestic industries. The World Bank, through its funding programs, facilitated this shift, prioritizing external trade over internal growth and enabling the intermediaries to dominate without fostering genuine economic development. Colonial legacies have further entrenched this collapse, with institutions like KEPSA KENYA and KAM emerging as products of British colonial rule, where enterprise control was handed to mainly Asian companies and intermediaries who acted as brokers in a somewhat open market. These Asian companies capitalized on the post-colonial environment, monopolizing sectors and closing avenues for local production, especially under Moi’s policies and later escalations. The British colonial administration set the foundation by establishing these broker-dominated structures, ensuring that post-independence Kenya remained dependent on foreign-controlled trade networks, which deliberately suppressed indigenous manufacturing and innovation. Under Mwai Kibaki’s leadership from 2002 to 2013, the situation worsened as monopolies were solidified, making production costs prohibitively high through barriers like impossible licensing requirements and subcontracting practices. Government and state officials enriched themselves via contracts and policies that favored these intermediaries, using trade barriers to prevent local competition. Banks, as rich enterprises, profited immensely without producing anything tangible, circulating funds in a superficial economy. Similarly, insurance companies thrived amid a crumbling healthcare system, draining resources while supermarkets like Naivas Quickmart Kenya Carrefour Kenya Mathai Supermarkets and shops amassed profits despite widespread poverty, all by moving imported goods and erecting barriers to local alternatives. Lobby groups and corrupt bodies, such as the Kenya Bankers Association (KBA) KEPSA KENYA KAM AKI DLAK , have actively contributed by outsourcing production abroad - often to China - and posing as local producers, killing skills training for workers and turning factories into mere distribution hubs. These white-collar fraudsters influence policy by bribing MPigs in parliament’s toilets, to maintain their dominance, collaborating with religious leaders like Archbishop Anthony Muheria Jackson Ole Sapit Anglican Church of Kenya KENYA CONFERENCE OF CATHOLIC BISHOPS(KCCB) who are bribed to agitate against local products like traditional alcohol, promoting bans that suppress innovation and responsible regulation. This religious overlay, rooted in colonial hangovers, stifles rational approaches to issues like HIV prevention and economic self-reliance, ensuring that elite brokers continue harvesting wealth while the broader Kenyan economy remains in survival mode, starved of oxygen for growth.

Francis Gaitho

22,203 görüntüleme • 10 ay önce