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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.

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Luma AI

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22,697 просмотров • 11 месяцев назад •via X (Twitter)

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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.
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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 просмотров • 8 месяцев назад

This is not camera footage. It is a Blender character with 8K skin, detailed wrinkles, wet eyes, facial controls and enough micro-detail to make your brain keep waiting for the person to behave like a person. HumanPro packages that skin workflow into a Blender add-on instead of making artists rebuild it from scratch every time. The interesting AI angle is not “AI made a realistic girl.” A reusable 3D human can keep the same face across thousands of shots, then be relit, reposed, animated and dropped into completely different scenes without the identity drifting every six frames. Add Claude through Blender MCP and the workflow gets stranger: the model can help assemble scenes, adjust cameras and lighting, inspect renders and correct obvious visual problems, while the character system handles the skin and facial structure. It still does not remove the artist. Someone has to control expression, motion, lighting and the exact moment realism quietly turns into a very expensive mannequin. Most AI influencer projects are still fighting prompt consistency one image at a time. A rigged digital human is less magical, but probably much closer to how this becomes an actual production system.
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This is not camera footage. It is a Blender character with 8K skin, detailed wrinkles, wet eyes, facial controls and enough micro-detail to make your brain keep waiting for the person to behave like a person. HumanPro packages that skin workflow into a Blender add-on instead of making artists rebuild it from scratch every time. The interesting AI angle is not “AI made a realistic girl.” A reusable 3D human can keep the same face across thousands of shots, then be relit, reposed, animated and dropped into completely different scenes without the identity drifting every six frames. Add Claude through Blender MCP and the workflow gets stranger: the model can help assemble scenes, adjust cameras and lighting, inspect renders and correct obvious visual problems, while the character system handles the skin and facial structure. It still does not remove the artist. Someone has to control expression, motion, lighting and the exact moment realism quietly turns into a very expensive mannequin. Most AI influencer projects are still fighting prompt consistency one image at a time. A rigged digital human is less magical, but probably much closer to how this becomes an actual production system.

Rina

109,768 просмотров • 2 месяцев назад

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.
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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 просмотров • 1 год назад

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 👇
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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

13,979 просмотров • 2 месяцев назад

📖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.
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📖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⌚️

14,015 просмотров • 2 месяцев назад

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.
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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

375,160 просмотров • 3 лет назад

Would you underestimate her just because she wears a school uniform? GPT Image 2 + Seedance 2.0 on Sjolt Try Canvas: prompt Character Identity Lock (Highest Priority): Use the exact same young East Asian woman from the provided reference character sheet. Preserve 100% identical facial features, face shape, eye shape, nose, lips, skin tone, hairstyle, hair color, proportions, and overall identity throughout the entire video. Do not redesign, reinterpret, or substitute the character. She must remain instantly recognizable as the same person from the reference image. She has shoulder-length wavy silver-gray hair with subtle blue undertones, bright expressive eyes, fair skin, and a confident slight smile that naturally transitions into a focused, determined combat expression. She wears the identical navy blue Korean high school uniform blazer over a gray sweater vest, white collared shirt, striped tie, and matching school skirt from the reference character sheet. Video Prompt: A cinematic, hyper-realistic action sequence inside a chaotic South Korean high school classroom. The classroom is filled with overturned desks, scattered chairs, flying notebooks, broken pencils, and papers drifting through the air. Bright natural daylight streams through large classroom windows, creating realistic highlights, soft shadows, and cinematic contrast. The young female student moves with incredible speed, confidence, and precision as she expertly defends herself against multiple aggressive male students wearing matching Korean school uniforms. Every movement is fluid, athletic, and grounded in realistic martial arts choreography. The camera remains highly dynamic, featuring cinematic handheld tracking shots, fast push-ins, orbit shots, dramatic slow-motion moments, whip pans, low-angle hero shots, and close-up impact shots. Capture rapid combinations of punches, clean high kicks, evasive footwork, parries, elbow strikes, blocks, and throws. Desks slide across the floor, chairs topple over, and dust particles catch the sunlight, emphasizing the intensity of the action. Maintain a high shutter-speed action-photography aesthetic with crisp motion detail, subtle motion blur only during extremely fast movements, physically accurate body mechanics, realistic cloth simulation, natural hair physics, authentic facial expressions, and believable impact reactions. Keep the camera frequently returning to sharp close-ups of her face to reinforce character continuity and emotional intensity. Her silver-gray hair flows naturally with every movement while her determined eyes remain locked on her opponents. Photorealistic cinematic quality, 4K HDR, ultra-detailed skin textures, realistic lighting, volumetric daylight, physically based rendering, shallow depth of field during close-ups, blockbuster Korean action film aesthetic, empowering heroine energy, consistent facial identity throughout every frame, no face drift, no character variation, no animation-style exaggeration.
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Would you underestimate her just because she wears a school uniform? GPT Image 2 + Seedance 2.0 on Sjolt Try Canvas: prompt Character Identity Lock (Highest Priority): Use the exact same young East Asian woman from the provided reference character sheet. Preserve 100% identical facial features, face shape, eye shape, nose, lips, skin tone, hairstyle, hair color, proportions, and overall identity throughout the entire video. Do not redesign, reinterpret, or substitute the character. She must remain instantly recognizable as the same person from the reference image. She has shoulder-length wavy silver-gray hair with subtle blue undertones, bright expressive eyes, fair skin, and a confident slight smile that naturally transitions into a focused, determined combat expression. She wears the identical navy blue Korean high school uniform blazer over a gray sweater vest, white collared shirt, striped tie, and matching school skirt from the reference character sheet. Video Prompt: A cinematic, hyper-realistic action sequence inside a chaotic South Korean high school classroom. The classroom is filled with overturned desks, scattered chairs, flying notebooks, broken pencils, and papers drifting through the air. Bright natural daylight streams through large classroom windows, creating realistic highlights, soft shadows, and cinematic contrast. The young female student moves with incredible speed, confidence, and precision as she expertly defends herself against multiple aggressive male students wearing matching Korean school uniforms. Every movement is fluid, athletic, and grounded in realistic martial arts choreography. The camera remains highly dynamic, featuring cinematic handheld tracking shots, fast push-ins, orbit shots, dramatic slow-motion moments, whip pans, low-angle hero shots, and close-up impact shots. Capture rapid combinations of punches, clean high kicks, evasive footwork, parries, elbow strikes, blocks, and throws. Desks slide across the floor, chairs topple over, and dust particles catch the sunlight, emphasizing the intensity of the action. Maintain a high shutter-speed action-photography aesthetic with crisp motion detail, subtle motion blur only during extremely fast movements, physically accurate body mechanics, realistic cloth simulation, natural hair physics, authentic facial expressions, and believable impact reactions. Keep the camera frequently returning to sharp close-ups of her face to reinforce character continuity and emotional intensity. Her silver-gray hair flows naturally with every movement while her determined eyes remain locked on her opponents. Photorealistic cinematic quality, 4K HDR, ultra-detailed skin textures, realistic lighting, volumetric daylight, physically based rendering, shallow depth of field during close-ups, blockbuster Korean action film aesthetic, empowering heroine energy, consistent facial identity throughout every frame, no face drift, no character variation, no animation-style exaggeration.

Sharon Riley

26,184 просмотров • 2 месяцев назад

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)
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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

10,326 просмотров • 1 год назад

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)
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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

97,516 просмотров • 1 год назад

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
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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 просмотров • 4 месяцев назад

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.
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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

52,849 просмотров • 1 год назад

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.
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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

17,500 просмотров • 5 месяцев назад

EVERYONE PROMPTS THE ACTION. ALMOST NOBODY LOCKS THE IDENTITY — WHICH IS WHY TWO-CHARACTER SCENES FALL APART. Two freerunners racing across Tokyo rooftops, eight cuts, corkscrews over a rooftop gap at the end. The parkour is the easy part. Keeping them two separate people who never blend into each other is the part that actually breaks. Here's the full prompt built that way. Attach two reference photos as image_1 and image_2, and the same structure works for any multi-character action piece: FORMAT: 15 seconds, 16:9, 1080p, 8-cut cinematic ultra-advanced parkour footage. CHARACTERS: Two realistic individuals from image_1 and image_2. Use the attached images as absolute character references, and fully maintain the facial features, hairstyles, hair colors, skin textures, body types, height differences, outfits, color schemes, and age appearances of each person across all cuts. No altering into different people, face swaps, outfit changes, hairstyle changes, or mixing of the two individuals' features. SETTING: A sunny modern Japanese city reminiscent of Tokyo, Shibuya, and Yokohama — rooftops, alleys, staircases, railings, pipes, concrete walls. The two protagonists, as equals, race through at high speed running side by side, following, crossing paths, and coordinating. CUTS: 1. (00:00–00:01.60) Low-angle rear tracking. Both accelerate side by side and simultaneously kong vault over separate obstacles. 2. (00:01.60–00:03.40) Front low-angle. One wall runs the left wall, the other the right, then tic-tac to cross in midair and land on opposite rooftops. 3. (00:03.40–00:05.20) Lateral tracking. Consecutive precision jumps, then cat leaps to grab and climb a high wall. 4. (00:05.20–00:07.20) Rooftop tracking. The leader dash vaults, the trailer websters over the gap, then they swap front and back positions. 5. (00:07.20–00:09.20) Overhead moving camera. Both dive roll, then run side by side to speed vault a long railing. 6. (00:09.20–00:11.30) Handheld retreating from the front. One underbars, the other side flips, conquering the obstacle simultaneously. 7. (00:11.30–00:13.20) Drone from diagonal rear above. Both palm spin off left and right walls, kong vault, accelerate into the final jump. 8. (00:13.20–00:15.00) Climax. Both leap a large rooftop gap, each doing a corkscrew, camera circling them in midair as they land on separate rooftop edges — then run side by side into the distance. QUALITY: Live-action film quality. World-championship-level smooth freerunning. Realistic center-of-gravity shifts, muscle movement, natural landing impacts, swaying hair and clothing. Sharp background, natural motion blur only during high-speed movement. PROHIBITED: Facial distortion, altering into different people, face or body swaps, outfit changes, hairstyle changes, body type changes, limb multiplication, duplicates, body fusion, penetration, warping, floating, unnatural landings, anime style, CG style. A few things worth noticing about why it's built this way: The character block does identity work three separate times — the reference images, the "fully maintain" list, and the prohibited list at the end. That redundancy isn't padding; each one closes a different door the model tends to walk through. The prohibited list names the exact failure modes — face swaps, body fusion, limb multiplication. Telling the model what not to do is more effective here than describing what you want, because these are the specific ways two-character scenes collapse. Every cut assigns each person a distinct action — one wall runs left, the other right; one underbars, the other side flips. Giving them separate roles keeps them functionally two people, so the model can't average them into one. And the cuts are individually timed and framed. Long continuous motion is where identity drift creeps in — breaking it into eight discrete shots gives the model less room to blend them. Made in Seedance 2.0.
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EVERYONE PROMPTS THE ACTION. ALMOST NOBODY LOCKS THE IDENTITY — WHICH IS WHY TWO-CHARACTER SCENES FALL APART. Two freerunners racing across Tokyo rooftops, eight cuts, corkscrews over a rooftop gap at the end. The parkour is the easy part. Keeping them two separate people who never blend into each other is the part that actually breaks. Here's the full prompt built that way. Attach two reference photos as image_1 and image_2, and the same structure works for any multi-character action piece: FORMAT: 15 seconds, 16:9, 1080p, 8-cut cinematic ultra-advanced parkour footage. CHARACTERS: Two realistic individuals from image_1 and image_2. Use the attached images as absolute character references, and fully maintain the facial features, hairstyles, hair colors, skin textures, body types, height differences, outfits, color schemes, and age appearances of each person across all cuts. No altering into different people, face swaps, outfit changes, hairstyle changes, or mixing of the two individuals' features. SETTING: A sunny modern Japanese city reminiscent of Tokyo, Shibuya, and Yokohama — rooftops, alleys, staircases, railings, pipes, concrete walls. The two protagonists, as equals, race through at high speed running side by side, following, crossing paths, and coordinating. CUTS: 1. (00:00–00:01.60) Low-angle rear tracking. Both accelerate side by side and simultaneously kong vault over separate obstacles. 2. (00:01.60–00:03.40) Front low-angle. One wall runs the left wall, the other the right, then tic-tac to cross in midair and land on opposite rooftops. 3. (00:03.40–00:05.20) Lateral tracking. Consecutive precision jumps, then cat leaps to grab and climb a high wall. 4. (00:05.20–00:07.20) Rooftop tracking. The leader dash vaults, the trailer websters over the gap, then they swap front and back positions. 5. (00:07.20–00:09.20) Overhead moving camera. Both dive roll, then run side by side to speed vault a long railing. 6. (00:09.20–00:11.30) Handheld retreating from the front. One underbars, the other side flips, conquering the obstacle simultaneously. 7. (00:11.30–00:13.20) Drone from diagonal rear above. Both palm spin off left and right walls, kong vault, accelerate into the final jump. 8. (00:13.20–00:15.00) Climax. Both leap a large rooftop gap, each doing a corkscrew, camera circling them in midair as they land on separate rooftop edges — then run side by side into the distance. QUALITY: Live-action film quality. World-championship-level smooth freerunning. Realistic center-of-gravity shifts, muscle movement, natural landing impacts, swaying hair and clothing. Sharp background, natural motion blur only during high-speed movement. PROHIBITED: Facial distortion, altering into different people, face or body swaps, outfit changes, hairstyle changes, body type changes, limb multiplication, duplicates, body fusion, penetration, warping, floating, unnatural landings, anime style, CG style. A few things worth noticing about why it's built this way: The character block does identity work three separate times — the reference images, the "fully maintain" list, and the prohibited list at the end. That redundancy isn't padding; each one closes a different door the model tends to walk through. The prohibited list names the exact failure modes — face swaps, body fusion, limb multiplication. Telling the model what not to do is more effective here than describing what you want, because these are the specific ways two-character scenes collapse. Every cut assigns each person a distinct action — one wall runs left, the other right; one underbars, the other side flips. Giving them separate roles keeps them functionally two people, so the model can't average them into one. And the cuts are individually timed and framed. Long continuous motion is where identity drift creeps in — breaking it into eight discrete shots gives the model less room to blend them. Made in Seedance 2.0.

Nexlow

114,432 просмотров • 2 месяцев назад

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
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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 🇮🇳

17,862 просмотров • 5 месяцев назад

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
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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 просмотров • 2 лет назад

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)
0:19

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

19,054 просмотров • 10 месяцев назад

A 24-year-old built two AI girls with Claude and now clears $21,800 a month from them. The build took 15 days. He trained separate LoRAs for both girls, locked their identity seeds, and kept small imperfections on purpose: a loose strand of hair, tiny skin marks, slightly uneven framing. Perfect symmetry gets flagged. Small inconsistencies make them look real. He posts 5 times a day across TikTok, Instagram and X. Morning routines, gym sessions, mirror videos, outfit changes, pool clips, and videos of the two girls together. The content is designed so they look like two real friends who actually live in the same world. The smartest part is that the accounts interact with each other. One girl comments on the other’s posts, appears in her videos, and references things they supposedly did together. Followers stop seeing them as two AI models and start following the relationship between the characters. Within three months they crossed 312,000 followers combined and started receiving hundreds of DMs every night. The private channel sits at $25 a month, while an AI memory agent keeps track of every conversation, previous message, favorite post, and personal detail each follower has shared. Replies come back in under 30 seconds. The agent checks the user's previous conversations before answering, so the response feels consistent with the personality of the girl they are talking to instead of sounding like another generic AI chatbot. By the end of month three, the two accounts were generating $13,900 from subscriptions and private chats, another $5,700 from brand deals, and around $2,200 from digital products. The brands came after the audience started growing: clothing companies, beauty products, fitness brands, and lifestyle products wanted access to the same audience that was already following the two characters every day. The Claude stack that locked them: 1Full identity, personality, lighting, camera style and body proportions locked into separate character systems. 2Separate LoRAs trained only on each girl's approved character frames. 3Apartment, bedroom, gym and outdoor locations generated once and reused to keep the world consistent. 4Every video built around natural movement, imperfect framing and small variations instead of polished AI-perfect shots. 5Memory agent connected to the conversations so both girls remember what followers previously said. 6Upscaling, face consistency and final post-processing before everything goes live. The first girl brings people into the account. The second gives them another character to follow, another story to watch, and another reason to come back. The content gets them interested. The relationship between the two characters keeps them watching. The memory agent turns that attention into recurring revenue.
0:12

A 24-year-old built two AI girls with Claude and now clears $21,800 a month from them. The build took 15 days. He trained separate LoRAs for both girls, locked their identity seeds, and kept small imperfections on purpose: a loose strand of hair, tiny skin marks, slightly uneven framing. Perfect symmetry gets flagged. Small inconsistencies make them look real. He posts 5 times a day across TikTok, Instagram and X. Morning routines, gym sessions, mirror videos, outfit changes, pool clips, and videos of the two girls together. The content is designed so they look like two real friends who actually live in the same world. The smartest part is that the accounts interact with each other. One girl comments on the other’s posts, appears in her videos, and references things they supposedly did together. Followers stop seeing them as two AI models and start following the relationship between the characters. Within three months they crossed 312,000 followers combined and started receiving hundreds of DMs every night. The private channel sits at $25 a month, while an AI memory agent keeps track of every conversation, previous message, favorite post, and personal detail each follower has shared. Replies come back in under 30 seconds. The agent checks the user's previous conversations before answering, so the response feels consistent with the personality of the girl they are talking to instead of sounding like another generic AI chatbot. By the end of month three, the two accounts were generating $13,900 from subscriptions and private chats, another $5,700 from brand deals, and around $2,200 from digital products. The brands came after the audience started growing: clothing companies, beauty products, fitness brands, and lifestyle products wanted access to the same audience that was already following the two characters every day. The Claude stack that locked them: 1Full identity, personality, lighting, camera style and body proportions locked into separate character systems. 2Separate LoRAs trained only on each girl's approved character frames. 3Apartment, bedroom, gym and outdoor locations generated once and reused to keep the world consistent. 4Every video built around natural movement, imperfect framing and small variations instead of polished AI-perfect shots. 5Memory agent connected to the conversations so both girls remember what followers previously said. 6Upscaling, face consistency and final post-processing before everything goes live. The first girl brings people into the account. The second gives them another character to follow, another story to watch, and another reason to come back. The content gets them interested. The relationship between the two characters keeps them watching. The memory agent turns that attention into recurring revenue.

genuenci

678,031 просмотров • 1 месяц назад