Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

This is character consistency with Ray3. Subjects maintain identity as they travel through environments or uphold features within spatial changes. Characters remain clear and coherent across every frame, preserving fidelity so scenes retain realism, continuity and visual depth.

Luma AI

188,460 subscribers

22,697 görüntüleme • 9 ay önce •via X (Twitter)

Bilim & Teknoloji Eğitim Sanat

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

This is preserved anatomy with Ray3. From faces and hands to bodies in motion, every form stays true. Humans and creatures remain clear and coherent in every action. Retain character structure with high fidelity, holding details together even through complex cinematic scenes.

This is preserved anatomy with Ray3. From faces and hands to bodies in motion, every form stays true. Humans and creatures remain clear and coherent in every action. Retain character structure with high fidelity, holding details together even through complex cinematic scenes.

Luma AI

60,389 görüntüleme • 9 ay önce

This is complex crowds with Ray3. From packed stadiums to bustling streets, large groups remain coherent. Every subject stays clear and consistent as scenes unfold. High fidelity preserves scale, motion, and detail ensuring realism across even the most complicated scenarios.

This is complex crowds with Ray3. From packed stadiums to bustling streets, large groups remain coherent. Every subject stays clear and consistent as scenes unfold. High fidelity preserves scale, motion, and detail ensuring realism across even the most complicated scenarios.

Luma AI

407,262 görüntüleme • 9 ay önce

Most video tools can generate clips. Very few can maintain identity. That has been the real bottleneck in AI video creation. Kling O1 changes that. For the first time, creators can carry a character, style, and visual language across scenes without constant fixes. You can reference past clips, assets, or images and the output stays consistently on-model. No visual drift. No rework loops. No “this doesn’t look like the last shot” moments. It feels less like prompting a tool and more like working with a creative collaborator that remembers context. The impact is practical, not theoretical: → Faster production cycles → Lower iteration costs → Noticeably higher output quality This is what mature AI tooling looks like. Not louder features. Not bigger claims. Just reliability where it actually matters. Consistency is no longer the problem.

Most video tools can generate clips. Very few can maintain identity. That has been the real bottleneck in AI video creation. Kling O1 changes that. For the first time, creators can carry a character, style, and visual language across scenes without constant fixes. You can reference past clips, assets, or images and the output stays consistently on-model. No visual drift. No rework loops. No “this doesn’t look like the last shot” moments. It feels less like prompting a tool and more like working with a creative collaborator that remembers context. The impact is practical, not theoretical: → Faster production cycles → Lower iteration costs → Noticeably higher output quality This is what mature AI tooling looks like. Not louder features. Not bigger claims. Just reliability where it actually matters. Consistency is no longer the problem.

Darshal Jaitwar

141,038 görüntüleme • 6 ay önce

Depth Any Video with Scalable Synthetic Data AI physicists and chemists continue to make strides in depth estimation from video. Check out this new paper featuring some impressive examples. See the thread for more details (unfortunately no code yet). Abstract: Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introduce Depth Any Video, a model that tackles the challenge through two key innovations. First, we develop a scalable synthetic data pipeline, capturing real-time video depth data from diverse game environments, yielding 40,000 video clips of 5-second duration, each with precise depth annotations. Second, we leverage the powerful priors of generative video diffusion models to handle real-world videos effectively, integrating advanced techniques such as rotary position encoding and flow matching to further enhance flexibility and efficiency. Unlike previous models, which are limited to fixed-length video sequences, our approach introduces a novel mixed-duration training strategy that handles videos of varying lengths and performs robustly across different frame rates 0 - even on single frames. At inference, we propose a depth interpolation method that enables our model to infer high-resolution video depth across sequences of up to 150 frames. Our model outperforms all previous generative depth models in terms of spatial accuracy and temporal consistency.

Depth Any Video with Scalable Synthetic Data AI physicists and chemists continue to make strides in depth estimation from video. Check out this new paper featuring some impressive examples. See the thread for more details (unfortunately no code yet). Abstract: Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introduce Depth Any Video, a model that tackles the challenge through two key innovations. First, we develop a scalable synthetic data pipeline, capturing real-time video depth data from diverse game environments, yielding 40,000 video clips of 5-second duration, each with precise depth annotations. Second, we leverage the powerful priors of generative video diffusion models to handle real-world videos effectively, integrating advanced techniques such as rotary position encoding and flow matching to further enhance flexibility and efficiency. Unlike previous models, which are limited to fixed-length video sequences, our approach introduces a novel mixed-duration training strategy that handles videos of varying lengths and performs robustly across different frame rates 0 - even on single frames. At inference, we propose a depth interpolation method that enables our model to infer high-resolution video depth across sequences of up to 150 frames. Our model outperforms all previous generative depth models in terms of spatial accuracy and temporal consistency.

MrNeRF

27,428 görüntüleme • 1 yıl önce

📖THE STEP MOST CREATORS SKIP IS WHY THEIR AI ANIMATION LOOKS INCONSISTENT Consistency across clips doesn't come from prompting — it comes from the reference image. The pipeline, step by step: ▪ Start with ChatGPT Image 2 — generate a full character design sheet first, not just a single frame. Multiple angles, expressions, and outfit variations in one image keeps the character consistent across every scene ▪ Build a storyboard inside ChatGPT Image 2 as well — define each shot, camera angle, action, and mood before touching Seedance at all. This is the step most people skip and it's the reason clips look disconnected ▪ Define a color palette and lighting mood early — golden afternoon light, soft warm tones, dramatic shadows. Lock those values and repeat them across every prompt ▪ Take each storyboard frame into Seedance 2.0 as the reference image — one frame becomes one clip ▪ Write the Seedance prompt around the character action, not the scene description. The scene is already in the image. The prompt handles motion, camera behavior, and timing ▪ Keep clip duration between 4-6 seconds per shot — shorter clips give more control over pacing and reduce motion drift on character faces ▪ Match camera movement type across consecutive clips — if one shot dollies in, the next should hold or pull back, not dolly again The consistency across these frames comes from the character design sheet, not from luck. Seedance reads the reference image and the prompt together — if the reference is detailed enough, the output stays on-model. This video was created by ALOKXMEHTA 📥 tomorrow: the exact ChatGPT Image 2 prompt structure used to generate a multi-angle character design sheet like this one 🔖One article covers the entire workflow — it is pinned below, do not scroll past it.

📖THE STEP MOST CREATORS SKIP IS WHY THEIR AI ANIMATION LOOKS INCONSISTENT Consistency across clips doesn't come from prompting — it comes from the reference image. The pipeline, step by step: ▪ Start with ChatGPT Image 2 — generate a full character design sheet first, not just a single frame. Multiple angles, expressions, and outfit variations in one image keeps the character consistent across every scene ▪ Build a storyboard inside ChatGPT Image 2 as well — define each shot, camera angle, action, and mood before touching Seedance at all. This is the step most people skip and it's the reason clips look disconnected ▪ Define a color palette and lighting mood early — golden afternoon light, soft warm tones, dramatic shadows. Lock those values and repeat them across every prompt ▪ Take each storyboard frame into Seedance 2.0 as the reference image — one frame becomes one clip ▪ Write the Seedance prompt around the character action, not the scene description. The scene is already in the image. The prompt handles motion, camera behavior, and timing ▪ Keep clip duration between 4-6 seconds per shot — shorter clips give more control over pacing and reduce motion drift on character faces ▪ Match camera movement type across consecutive clips — if one shot dollies in, the next should hold or pull back, not dolly again The consistency across these frames comes from the character design sheet, not from luck. Seedance reads the reference image and the prompt together — if the reference is detailed enough, the output stays on-model. This video was created by ALOKXMEHTA 📥 tomorrow: the exact ChatGPT Image 2 prompt structure used to generate a multi-angle character design sheet like this one 🔖One article covers the entire workflow — it is pinned below, do not scroll past it.

Zentrix⌚️

12,846 görüntüleme • 15 gün önce

Seedance 2.0 - Advanced Workflows Series 14. Compositing and Layering Some shots may have many characters that need specific movements. When we start adding characters, it becomes very difficult to achieve the exact animation, gesture, or movement we are looking of all of them in a single generation. In addition, the more characters are added, the more rigid Seedance 2.0 animations become in order to maintain consistency. This is especially visible in styles with squash and stretch and rubber hose animations, where the characters deform their body parts as if they were made of rubber, or in highly expressive movement animations in anime. For these cases, it is better to work on each animation separately and recompose it at the end into a video. Workflow + Prompts 👇

Seedance 2.0 - Advanced Workflows Series 14. Compositing and Layering Some shots may have many characters that need specific movements. When we start adding characters, it becomes very difficult to achieve the exact animation, gesture, or movement we are looking of all of them in a single generation. In addition, the more characters are added, the more rigid Seedance 2.0 animations become in order to maintain consistency. This is especially visible in styles with squash and stretch and rubber hose animations, where the characters deform their body parts as if they were made of rubber, or in highly expressive movement animations in anime. For these cases, it is better to work on each animation separately and recompose it at the end into a video. Workflow + Prompts 👇

VoxelPlot

12,846 görüntüleme • 3 gün önce

Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation paper page: Large text-to-image diffusion models have exhibited impressive proficiency in generating high-quality images. However, when applying these models to video domain, ensuring temporal consistency across video frames remains a formidable challenge. This paper proposes a novel zero-shot text-guided video-to-video translation framework to adapt image models to videos. The framework includes two parts: key frame translation and full video translation. The first part uses an adapted diffusion model to generate key frames, with hierarchical cross-frame constraints applied to enforce coherence in shapes, textures and colors. The second part propagates the key frames to other frames with temporal-aware patch matching and frame blending. Our framework achieves global style and local texture temporal consistency at a low cost (without re-training or optimization). The adaptation is compatible with existing image diffusion techniques, allowing our framework to take advantage of them, such as customizing a specific subject with LoRA, and introducing extra spatial guidance with ControlNet. Extensive experimental results demonstrate the effectiveness of our proposed framework over existing methods in rendering high-quality and temporally-coherent videos.

Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation paper page: Large text-to-image diffusion models have exhibited impressive proficiency in generating high-quality images. However, when applying these models to video domain, ensuring temporal consistency across video frames remains a formidable challenge. This paper proposes a novel zero-shot text-guided video-to-video translation framework to adapt image models to videos. The framework includes two parts: key frame translation and full video translation. The first part uses an adapted diffusion model to generate key frames, with hierarchical cross-frame constraints applied to enforce coherence in shapes, textures and colors. The second part propagates the key frames to other frames with temporal-aware patch matching and frame blending. Our framework achieves global style and local texture temporal consistency at a low cost (without re-training or optimization). The adaptation is compatible with existing image diffusion techniques, allowing our framework to take advantage of them, such as customizing a specific subject with LoRA, and introducing extra spatial guidance with ControlNet. Extensive experimental results demonstrate the effectiveness of our proposed framework over existing methods in rendering high-quality and temporally-coherent videos.

AK

375,123 görüntüleme • 3 yıl önce

Today, let’s talk about the possibilities of We’re living in a generation where IP is exploding. Web2 and Web3 are all about IP and everything is tied to digital identity. Be it a PFP or a brand, it carries personality and value. So let’s use NFTs as an example. NFTs are built around IPs. Characters that build the community and the world of a project. (I’ve been in the NFT space for 3-4 years. So yes, I’ve seen the cycle.) Some questions : - Beyond the image, how do we bring utility to every character? - What stories do they hold? - What personalities do they have? - Can each one have a digital identity beyond just being a PFP? - And most importantly : how can it talk to millions of people without the owner replying to every message? That’s just difficult to scale. Exactly the reason why we are building To bring IPs to life. Every character deserves a story. Deserves to connect emotionally anytime, with anyone. (Using daniel matsunaga 💎👋🏻💎 space io in this example)

Clement | Imaginary Ones | Bubio.ai

10,322 görüntüleme • 1 yıl önce

You can now create consistent characters with Google Veo 3 videos 🤯 These AG1 ads were created with MY FACE as the spokesperson. This character consistency technique will take any product... And turn it into personalized video ads with lip-sync. Perfect for creative strategists, e-comm brands & ad agencies. Just upload your face and get unlimited spokesperson videos with YOUR consistent character. The best part? Your character stays consistent through zooms → organized by multiple angles, product swaps, voice cloning, & scene variations. Here's what this workflow gives you: → your face as the spokesperson (Higgsfield or Ideogram) → perfect lip-sync with Veo 3 → product swapping with Flux Kontext → custom voice cloning with ElevenLabs Suddenly you're starring in every ad without filming anything. Prediction: we're 3 months away from everyone having AI spokespeople. Start building yours now. Want the full prompts & workflow? Comment "VEO3" + follow and I'll send it over (must be following so I can DM)

You can now create consistent characters with Google Veo 3 videos 🤯 These AG1 ads were created with MY FACE as the spokesperson. This character consistency technique will take any product... And turn it into personalized video ads with lip-sync. Perfect for creative strategists, e-comm brands & ad agencies. Just upload your face and get unlimited spokesperson videos with YOUR consistent character. The best part? Your character stays consistent through zooms → organized by multiple angles, product swaps, voice cloning, & scene variations. Here's what this workflow gives you: → your face as the spokesperson (Higgsfield or Ideogram) → perfect lip-sync with Veo 3 → product swapping with Flux Kontext → custom voice cloning with ElevenLabs Suddenly you're starring in every ad without filming anything. Prediction: we're 3 months away from everyone having AI spokespeople. Start building yours now. Want the full prompts & workflow? Comment "VEO3" + follow and I'll send it over (must be following so I can DM)

Mike Futia

97,347 görüntüleme • 11 ay önce

GPT-Image-2 + Seedance 2.0目前已成AI视频标配甚至可以根据给定图片推导过去和未来，制作storyboard，然后生成视频使用方法： 1️⃣ 随便找一张图 2️⃣ 给以下提示词，然后制作storyboard 用以下提示词👇： Create a 3×3 cinematic storyboard grid based on the uploaded reference image. Use the uploaded image as the central moment of the story: Frame 5 must represent the exact “t” moment, matching the subject, scene, mood, composition, costume, environment, lighting style, and emotional tone of the reference image. The storyboard must show what happened before and after this moment as a time-based visual timeline. FRAME STRUCTURE: Frame 1: t-30: Establishing shot, the wider environment before the main event begins. Frame 2: t-10: The subject approaches or prepares for the key moment. Frame 3: t-5: Tension builds, body language and atmosphere lead toward the reference image. Frame 4: t-1: Final instant before the reference image, close emotional or action transition. Frame 5: t: Recreate the uploaded reference image as the central key frame. Frame 6: t+1: Immediate reaction or continuation right after the key moment. Frame 7: t+5: Alternate angle showing the consequence of the moment. Frame 8: t+15: Candid transition frame, natural movement, emotional aftermath. Frame 9: t+30: Strong final cinematic frame that clearly resolves the scene. STYLE: Ultra-realistic cinematic storyboard, 3×3 grid layout, cohesive visual tone across all frames, consistent character identity, consistent costume, consistent environment, cinematic lighting, shallow depth of field, realistic camera angles, natural motion continuity, no text labels, no numbers, no arrows, no captions inside the image. 3️⃣ seedance2.0 一键成片

GPT-Image-2 + Seedance 2.0目前已成AI视频标配甚至可以根据给定图片推导过去和未来，制作storyboard，然后生成视频使用方法： 1️⃣ 随便找一张图 2️⃣ 给以下提示词，然后制作storyboard 用以下提示词👇： Create a 3×3 cinematic storyboard grid based on the uploaded reference image. Use the uploaded image as the central moment of the story: Frame 5 must represent the exact “t” moment, matching the subject, scene, mood, composition, costume, environment, lighting style, and emotional tone of the reference image. The storyboard must show what happened before and after this moment as a time-based visual timeline. FRAME STRUCTURE: Frame 1: t-30: Establishing shot, the wider environment before the main event begins. Frame 2: t-10: The subject approaches or prepares for the key moment. Frame 3: t-5: Tension builds, body language and atmosphere lead toward the reference image. Frame 4: t-1: Final instant before the reference image, close emotional or action transition. Frame 5: t: Recreate the uploaded reference image as the central key frame. Frame 6: t+1: Immediate reaction or continuation right after the key moment. Frame 7: t+5: Alternate angle showing the consequence of the moment. Frame 8: t+15: Candid transition frame, natural movement, emotional aftermath. Frame 9: t+30: Strong final cinematic frame that clearly resolves the scene. STYLE: Ultra-realistic cinematic storyboard, 3×3 grid layout, cohesive visual tone across all frames, consistent character identity, consistent costume, consistent environment, cinematic lighting, shallow depth of field, realistic camera angles, natural motion continuity, no text labels, no numbers, no arrows, no captions inside the image. 3️⃣ seedance2.0 一键成片

Jason Zhu

34,160 görüntüleme • 2 ay önce

Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.

Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.

MrNeRF

52,801 görüntüleme • 1 yıl önce

Space objects such as meteoroids and asteroids move across the solar system and can collide with both Earth and the Moon. However, the Moon shows far more visible impact marks on its surface. One main reason is that Earth has a thick atmosphere that protects the planet. When many meteoroids enter Earth’s atmosphere, they burn up due to friction before they can reach the ground. This process creates the bright streaks of light we call shooting stars. The Moon, however, has almost no atmosphere, so incoming space rocks are not slowed down or burned up. They travel directly to the lunar surface and strike it at very high speed. Each collision creates an impact crater, throwing dust and rocks outward from the impact point. Over billions of years, thousands of these impacts have covered the Moon with craters. Unlike Earth, the Moon also has no wind, water, or weather to erase these craters, so they remain visible for millions or even billions of years. This is why the Moon looks heavily cratered when we observe it from Earth through telescopes.

Space objects such as meteoroids and asteroids move across the solar system and can collide with both Earth and the Moon. However, the Moon shows far more visible impact marks on its surface. One main reason is that Earth has a thick atmosphere that protects the planet. When many meteoroids enter Earth’s atmosphere, they burn up due to friction before they can reach the ground. This process creates the bright streaks of light we call shooting stars. The Moon, however, has almost no atmosphere, so incoming space rocks are not slowed down or burned up. They travel directly to the lunar surface and strike it at very high speed. Each collision creates an impact crater, throwing dust and rocks outward from the impact point. Over billions of years, thousands of these impacts have covered the Moon with craters. Unlike Earth, the Moon also has no wind, water, or weather to erase these craters, so they remain visible for millions or even billions of years. This is why the Moon looks heavily cratered when we observe it from Earth through telescopes.

The Astronomy Guy

17,473 görüntüleme • 3 ay önce

🇨🇳 Another great Chinese Model, OmniHuman-1.5 from ByteDance Turns 1 image plus a voice track into expressive avatar video by pairing a System 1 and System 2 inspired planner with a Diffusion Transformer, Produces coherent motion for over 1 minute with moving camera and multi character scenes. Most avatar models move to the beat of the audio but miss meaning, so gestures feel generic and emotions feel shallow. The fix here is a Multimodal LLM planner that listens to the speech and drafts a structured plan describing intent, emotions, beats, and high level actions, which gives the motion engine clear semantic targets instead of only rhythm. The motion engine is a Multimodal Diffusion Transformer that fuses the plan with audio, the single reference image, and optional text prompts, then synthesizes continuous body, face, and head motion that matches both words and tone. A key trick is a Pseudo Last Frame, a synthetic target that summarizes the next expected state, which stabilizes fusion across modalities and keeps motion consistent over long spans. From just 1 image and speech, the system outputs speaking avatars with synchronized lips, context aware gestures, and continuous camera movement, and it also supports multi character interactions without manual choreography. Reported results show strong lip sync accuracy, high video quality, natural motion, and close match to text prompts, and the same setup works on nonhuman characters too.

Rohan Paul

63,859 görüntüleme • 10 ay önce

This is something truly heartbreaking. Seeing these voiceless animals being forced to pull loads far beyond their capacity, treated with such neglect and cruelty, shakes you from within. In today’s world, where so many alternatives exist, why are these innocent beings still not phased out from such brutal labor? They are made to work in the harshest environments, without rest, without care, without compassion. Just look at this male buffalo, struggling as he pulls wet sand from a canal. Every step he takes is filled with pain and exhaustion, yet he has no choice. No voice to cry for help. No way to escape. We often ignore these sights as “normal,” but this is not normal. This is suffering. This is injustice. My heart goes out to each one of these innocent souls who silently endure so much every single day. They don’t deserve this life of pain. They deserve dignity, kindness, and freedom. It’s time we question this. It’s time we raise our voices. Because if we don’t speak for them, who will? #AnimalCruelty #BeTheirVoice

This is something truly heartbreaking. Seeing these voiceless animals being forced to pull loads far beyond their capacity, treated with such neglect and cruelty, shakes you from within. In today’s world, where so many alternatives exist, why are these innocent beings still not phased out from such brutal labor? They are made to work in the harshest environments, without rest, without care, without compassion. Just look at this male buffalo, struggling as he pulls wet sand from a canal. Every step he takes is filled with pain and exhaustion, yet he has no choice. No voice to cry for help. No way to escape. We often ignore these sights as “normal,” but this is not normal. This is suffering. This is injustice. My heart goes out to each one of these innocent souls who silently endure so much every single day. They don’t deserve this life of pain. They deserve dignity, kindness, and freedom. It’s time we question this. It’s time we raise our voices. Because if we don’t speak for them, who will? #AnimalCruelty #BeTheirVoice

Vidit Sharma 🇮🇳

17,852 görüntüleme • 3 ay önce

It is official!! Shellz Orb NFTs will be able to dance, prance, and roam free through out !! Our prized Shinobi character has been integrated into their world amongst hundreds of premium and 3D based NFT projects!! We are, however, currently working to deliver more Shellz Orb characters so they can join in on the fun with #Shinobi and the rest. We would like to thank Nifty Island and their team for including us in this journey with them! Finally, we would like to thank our community! Their activity and grind allowed us to remain relevant during these times of extreme volatility to participate and activate when these amazing opportunities arise! #shellzup

It is official!! Shellz Orb NFTs will be able to dance, prance, and roam free through out !! Our prized Shinobi character has been integrated into their world amongst hundreds of premium and 3D based NFT projects!! We are, however, currently working to deliver more Shellz Orb characters so they can join in on the fun with #Shinobi and the rest. We would like to thank Nifty Island and their team for including us in this journey with them! Finally, we would like to thank our community! Their activity and grind allowed us to remain relevant during these times of extreme volatility to participate and activate when these amazing opportunities arise! #shellzup

Shellz Orb 🐢

12,950 görüntüleme • 2 yıl önce

Sora 2 + n8n is absolutely insane 🤯 This n8n automation generates entire UGC campaigns with the same AI creator across unlimited videos. All from one Airtable form. Perfect for DTC brands & agencies who need brand consistency in their AI ads without hiring real creators. Why this matters: Every AI video tool gives you a random person each time. You can't build multi-video campaigns because your "creator" changes in every clip. Sora 2 consistent characters solves this: Same AI creator → Different scenes → Unlimited videos The n8n workflow: → Fill out Airtable form once (select your character, describe scenes, choose quantity) → Claude AI generates professional Sora 2 prompts automatically → Sora 2 renders videos with your consistent character → Videos auto-upload to ImageKit CDN → Everything tracked in Airtable with shareable URLs No manual prompting. No file management. No different people in every video. What you can create: → 3-part testimonial series with the same person → Before/during/after transformation campaigns → Product tutorial sequences that feel cohesive → Entire ad creative libraries with your "brand ambassador" Track everything in Airtable: → Video status (queued → generating → complete) → Shareable URLs for each clip → Scene descriptions and prompts → Production-ready in 5-10 minutes Built 100% in n8n + Airtable. Want the complete template? > Comment "SORA" > Like this post And I'll send it over (must be following so I can DM)

Mike Futia

18,972 görüntüleme • 8 ay önce

mj style creator → 3×3 grid with nano banana pro → turn it into motion with hailuo 2.3 → final polish using topaz astra this simple workflow helps you think visually before you over generate. starting with a 3×3 grid lets you explore composition, mood and performance in one pass, instead of guessing shot by shot. from there, it’s much easier to choose which frames are worth pushing further, test variations and maintain consistency across scenes. turns your ideas into a clear live storyboard before moving into a full motion. great for a/b testing shots, refining actions and building stronger cinematic sequences with intention. made w x Hailuo AI-MiniMax Hub x Topaz Labs the guide structure is the same as the one i shared before ⬇️

mj style creator → 3×3 grid with nano banana pro → turn it into motion with hailuo 2.3 → final polish using topaz astra this simple workflow helps you think visually before you over generate. starting with a 3×3 grid lets you explore composition, mood and performance in one pass, instead of guessing shot by shot. from there, it’s much easier to choose which frames are worth pushing further, test variations and maintain consistency across scenes. turns your ideas into a clear live storyboard before moving into a full motion. great for a/b testing shots, refining actions and building stronger cinematic sequences with intention. made w x Hailuo AI-MiniMax Hub x Topaz Labs the guide structure is the same as the one i shared before ⬇️

DStudioproject

77,375 görüntüleme • 7 ay önce

▫️🧵I didn’t realize my original thread breaking all this down mysteriously vanished, so I had to reconstruct the entire thing from scratch. But here’s a spoiler: almost two years later- the Nova Festival footage still doesn’t make any sense. The terrain is still inconsistent, the vehicle placements are aren’t accurate and it’s even more obvious now this was filmed over several days on military bases. The irony here is brutal. GeoConfirmed accuses me of “spreading misinformation through insinuation.” ➤ I asked direct forensic questions about visual and spatial inconsistencies. ➤ He answered with speculation larping as geolocation—then wrapped it in the same insinuation tactics he claims to oppose. ➤ He says the videos are 600 meters apart. Based on what? Colored boxes and estimation lines drawn onto satellite photos—not forensic video continuity

▫️🧵I didn’t realize my original thread breaking all this down mysteriously vanished, so I had to reconstruct the entire thing from scratch. But here’s a spoiler: almost two years later- the Nova Festival footage still doesn’t make any sense. The terrain is still inconsistent, the vehicle placements are aren’t accurate and it’s even more obvious now this was filmed over several days on military bases. The irony here is brutal. GeoConfirmed accuses me of “spreading misinformation through insinuation.” ➤ I asked direct forensic questions about visual and spatial inconsistencies. ➤ He answered with speculation larping as geolocation—then wrapped it in the same insinuation tactics he claims to oppose. ➤ He says the videos are 600 meters apart. Based on what? Colored boxes and estimation lines drawn onto satellite photos—not forensic video continuity

Morgan

89,737 görüntüleme • 11 ay önce

Hi everyone! We are STEJAY HARBOR ⚓️ 🚢 We are a fanbase. A small group with big dreams, brought together by one shared purpose: to support, promote, and uplift Stejay across all platforms. As a dedicated fanbase, we are built on love and guided by loyalty. We exist to protect what we value, celebrate every moment, and stand firm through it all. We are committed to giving equal support, respect, and love to both Steven and JL. This is a Stejay-focused fanbase. Intentional, grounded, and here for the long journey. Every project, every effort, and every voice is rooted in genuine support and collective passion. For us, a harbor is more than a place. It is a safe space. A point of return. A place where you can rest, stay, and feel protected no matter how strong the tides get. That is what we want this fanbase to be for Stejay and for everyone who supports them. What started as admiration grew into something deeper. A shared feeling, a quiet understanding, a choice to stay. This fanbase is made up of people who found comfort, joy, and connection through Stejay, and chose to build something meaningful from it. We aim to create thoughtful and organized projects that amplify their presence across platforms. We work as one team to ensure consistency, impact, and purpose in everything we do. More than anything, we want to build a space where fans feel safe, welcomed, and understood. A place where excitement is shared, efforts are valued, and support is given wholeheartedly. A place where no one feels alone. We may be starting small, but we are growing with direction, discipline, and heart. We believe that even the smallest beginnings can create something lasting when it is built with sincerity. We believe in showing up consistently, supporting responsibly, and protecting what we stand for. Through every milestone and every challenge, we remain steady. We rise together. We move as one. We stay grounded and anchored no matter the tide. This is more than support. This is a fanbase built to stay. 🤍 #AnchoredWithStejay #STEVEN #JL #STEJAY #스티븐 #제이엘 #AHOF #아홉 AHOF Video Editor 🎥: Aurora

Hi everyone! We are STEJAY HARBOR ⚓️ 🚢 We are a fanbase. A small group with big dreams, brought together by one shared purpose: to support, promote, and uplift Stejay across all platforms. As a dedicated fanbase, we are built on love and guided by loyalty. We exist to protect what we value, celebrate every moment, and stand firm through it all. We are committed to giving equal support, respect, and love to both Steven and JL. This is a Stejay-focused fanbase. Intentional, grounded, and here for the long journey. Every project, every effort, and every voice is rooted in genuine support and collective passion. For us, a harbor is more than a place. It is a safe space. A point of return. A place where you can rest, stay, and feel protected no matter how strong the tides get. That is what we want this fanbase to be for Stejay and for everyone who supports them. What started as admiration grew into something deeper. A shared feeling, a quiet understanding, a choice to stay. This fanbase is made up of people who found comfort, joy, and connection through Stejay, and chose to build something meaningful from it. We aim to create thoughtful and organized projects that amplify their presence across platforms. We work as one team to ensure consistency, impact, and purpose in everything we do. More than anything, we want to build a space where fans feel safe, welcomed, and understood. A place where excitement is shared, efforts are valued, and support is given wholeheartedly. A place where no one feels alone. We may be starting small, but we are growing with direction, discipline, and heart. We believe that even the smallest beginnings can create something lasting when it is built with sincerity. We believe in showing up consistently, supporting responsibly, and protecting what we stand for. Through every milestone and every challenge, we remain steady. We rise together. We move as one. We stay grounded and anchored no matter the tide. This is more than support. This is a fanbase built to stay. 🤍 #AnchoredWithStejay #STEVEN #JL #STEJAY #스티븐 #제이엘 #AHOF #아홉 AHOF Video Editor 🎥: Aurora

SteJay Harbor

117,547 görüntüleme • 2 ay önce

Google 3D Maps aren’t as accurate as you think. Everyone knows Google dominates mapping—but here’s what they won’t tell you. Their 3D maps are built with car-mounted cameras, capturing images every ~10 meters. That’s fine for basic navigation, but it’s nowhere near enough for AR, robotics, autonomous systems, or AI-driven spatial intelligence. Now, imagine a map so detailed it captures the world at sub-5cm accuracy. That’s OVRMaps. 🌐 OVER 3D maps are built with 400-1,000 images per 300 sqm, taken from multiple angles at pedestrian level. The result? A new era of hyper-precise localization that changes everything. Why does this matter? ↳ VPS that actually works – Real-world AR anchoring, digital twins, and AI-powered spatial computing with pinpoint accuracy. ↳ Next-gen precision – Essential for ride-sharing, robotics, smart cities, and asset tracking—where even a small error makes a huge difference. ↳ AI-Driven Spatial Intelligence – Our maps fuel Large Geo-Spatial Models (LGMs), the AI revolution enabling machines to understand, navigate, and interact with the real world. ↳ The Spatial Computing revolution – Merging immersive experiences with real-world precision, unlocking applications beyond what Google’s dataset can support. OVRMaps are built for the future. 🌍 Start mapping today:

Google 3D Maps aren’t as accurate as you think. Everyone knows Google dominates mapping—but here’s what they won’t tell you. Their 3D maps are built with car-mounted cameras, capturing images every ~10 meters. That’s fine for basic navigation, but it’s nowhere near enough for AR, robotics, autonomous systems, or AI-driven spatial intelligence. Now, imagine a map so detailed it captures the world at sub-5cm accuracy. That’s OVRMaps. 🌐 OVER 3D maps are built with 400-1,000 images per 300 sqm, taken from multiple angles at pedestrian level. The result? A new era of hyper-precise localization that changes everything. Why does this matter? ↳ VPS that actually works – Real-world AR anchoring, digital twins, and AI-powered spatial computing with pinpoint accuracy. ↳ Next-gen precision – Essential for ride-sharing, robotics, smart cities, and asset tracking—where even a small error makes a huge difference. ↳ AI-Driven Spatial Intelligence – Our maps fuel Large Geo-Spatial Models (LGMs), the AI revolution enabling machines to understand, navigate, and interact with the real world. ↳ The Spatial Computing revolution – Merging immersive experiences with real-world precision, unlocking applications beyond what Google’s dataset can support. OVRMaps are built for the future. 🌍 Start mapping today:

Over the Reality 🌐

1,331,567 görüntüleme • 1 yıl önce