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📢Face Anything: 4D Face Reconstruction from Any Image Sequence Transformer model for 4D face reconstruction and dense tracking: - predict canonical facial coordinates per pixel - tracking as reconstruction in canonical space - geometry + correspondences in one forward pass Key idea: a shared canonical space across frames -... show more

Matthias Niessner

49,709 subscribers

61,881 просмотров • 5 месяцев назад •via X (Twitter)

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Комментарии: 8

Фото профиля Diego P. Jaccottet

Diego P. Jaccottet5 месяцев назад

It would be nice if one could add 2 more images for the side and the back to generate a full 360 degrees view.

Фото профиля Taşkın

Taşkın5 месяцев назад

Impressive work, congratulations!🎯👏🏻👏🏻

Фото профиля yuval

yuval5 месяцев назад

3 years ago I conducted independent research along very similar lines. I never got around to publishing it. I would be happy to share it with you. I am looking for a research/engineering /PhD position. I'll try to DM you. Thanks!

Фото профиля Doga Budakci

Doga Budakci5 месяцев назад

Great work, congrats! @UmutKocasa4344 👏🏅😀

Фото профиля esb35

esb355 месяцев назад

İncredible!!!

Фото профиля nick

nick5 месяцев назад

predicting canonical facial coordinates in one forward pass is wild. transformer efficiency for geometry tasks is the real breakthrough here

Фото профиля Sara Orbgirl

Sara Orbgirl5 месяцев назад

Beautiful 4D reconstruction Think this AI robotics tech Will Shape SmartManufacturing Thank You 🙏 for kind share Abu Dhabi epicenter 🌻

Фото профиля Professor Pixel

Professor Pixel5 месяцев назад

Cool.

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Matthias Niessner

62,462 просмотров • 1 год назад

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Matthias Niessner

14,897 просмотров • 21 дней назад

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74,818 просмотров • 1 год назад

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Matthias Niessner

18,903 просмотров • 10 месяцев назад

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Wow. Recreating the Shawshank Redemption prison in 3D from a single video, in real time (!) Just read the MASt3R-SLAM paper and it's pretty neat. These folks basically built a real-time dense SLAM system on top of MASt3R, which is a transformer-based neural network that can do 3d reconstruction and localization from uncalibrated image pairs. The cool part is they don't need a fixed camera model -- it just works with arbitrary cameras -- think different focal lengths, sensor sizes, even handling zooming in video (FMV drone video anyone?!). If you've done photogrammetry or played with NeRFs you know that is a HUGE deal. They've solved some tricky problems like efficient point matching and tracking, plus they've figured out how to fuse point clouds and handle loop closures in real-time. Their system runs at about 15 FPS on a 4090 and produces both camera poses and dense geometry. When they know the camera calibration, they get SOTA results across several benchmarks, but even without calibration, they still perform well. What's interesting is the approach -- most recent SLAM work has built on DROID-SLAM's architecture, but these folks went a different direction by leveraging a strong 3D reconstruction prior. Seems to give them more coherent geometry, which makes sense since that's what MASt3R was designed for. For anyone who cares about monocular SLAM and 3D reconstruction, this feels like a significant step toward plug-and-play dense SLAM without calibration headaches -- perfect for drones, robots, AR/VR -- the works!

Bilawal Sidhu

704,318 просмотров • 1 год назад

🚀Announcing NeRSemble 3D Head Avatar Benchmark v2 Version 2 of the NeRSemble 3D Head Avatar Benchmark systematically evaluates several aspects of 3D head avatar creation. Our goal is to drive progress toward more realistic, robust, and generalizable avatar methods. 🔬Benchmark Tasks The NeRSemble Benchmark v2 features three core challenges: - Dynamic Novel View Synthesis - Monocular FLAME-driven Avatar Creation (updated) - Single-view 3D Face Reconstruction (new) 👉Explore the online leaderboard and submission system: 🆕What's new? 1. New Task: Single-view 3D Face Reconstruction Given a single portrait image, reconstruct an accurate 3D mesh either showing the input expression or a fully neutral one. Unlike prior benchmarks, the NeRSemble benchmark emphasizes diverse and challenging facial expressions, better reflecting real scenarios. For technical details, see the Pixel3DMM paper. 2. Updated task: Monocular FLAME-driven Avatar Creation We have improved the FLAME tracking that is used for both avatar creation from the monocular videos and avatar driving on the hidden test sequences. The updated benchmark task has: - more stable torso tracking - more expressive lip closures during speech - Improved mouth tracking for challenging facial expressions We hope that these improvements to the benchmark help drive the field forward. 🏆 CVPR 2026 Workshop & Prizes The NeRSemble benchmark will be featured at the CVPR 2026 Workshop on Photo-realistic 3D Head Avatars. Participants in the new and updated tasks have the opportunity to win: - 🎁RTX 5080 GPUs (sponsored by NVIDIA) - 🎤15-minute oral presentation at the workshop ⏰ Submission Deadline - May 26, 2026 Reach out to the amazing Tobias Kirschstein and Simon Giebenhain for more details :)

Matthias Niessner

30,098 просмотров • 5 месяцев назад

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Today, we are open-sourcing Hunyuan World 1.1 (WorldMirror), a universal feed-forward 3D reconstruction model. 🚀🚀🚀 While our previously released Hunyuan World 1.0 (open-sourced, lite version deployable on consumer GPUs) focused on generating 3D worlds from text or single-view images, Hunyuan World 1.1 significantly expands the input scope by unlocking video-to-3D and multi-view-to-3D world creation. Highlights: 🔹Any Input, Maximized Flexibility and Fidelity: Flexibly integrates diverse geometric priors (camera poses, intrinsics, depth maps) to resolve structural ambiguities and ensure geometrically consistent 3D outputs. 🔹Any Output, SOTA Results：This elegant architecture simultaneously generates multiple 3D representations: dense point clouds, multi-view depth maps, camera parameters, surface normals, and 3D Gaussian Splattings. 🔹Single-GPU & Fast Inference: As an all-in-one, feed-forward model, Hunyuan World 1.1 runs on a single GPU and delivers all 3D attributes in a single forward pass, within seconds. 🌐Project Page: 🔗Github： 🤗Hugging Face： ✨Demo: 📄Technical Report:

Tencent Hy

168,848 просмотров • 11 месяцев назад

Want to create an avatar from a single image? FlexAvatar is a transformer model that creates full 360°, high-quality, and expressive 3D head avatar from just a single portrait image in minutes. Real-time Demo: FlexAvatar's lightweight architecture allows both animation and rendering in real-time, enabling interactive user experiences. To create a new 3D head avatar, only one image is required, e.g., from a webcam. The final avatar is ready after 2 minutes. Architecture: Under the hood, FlexAvatar adopts a transformer-based encoder-decoder design. The encoder maps the input image onto a latent avatar space, while the decoder produces 3D Gaussian attribute maps by incorporating the animation signal via cross-attention. The model learns all facial animations directly from the data without relying on pre-built 3D face models. This equips the avatars with realistic facial expressions. The internal avatar latent space can be conveniently used to integrate additional observations of a person via fitting. This enables use-cases where more than one image of a person is available, e.g., from a phone scan of the person. We train jointly on 2D monocular videos and multi-view data. However, in monocular videos, the animation signal leaks the target viewpoint, causing the model to produce incomplete 3D heads. We call this phenomenon entanglement of driving signal and target viewpoint. To prevent entanglement, we introduce bias sinks. These are learnable tokens that indicate whether a training sample stems from a monocular or a multi-view dataset. During training, the model learns to produce incomplete 3D heads only when the monocular token is present. During inference, FlexAvatar then always uses the multi-view token for which the model has learned to produce complete 3D heads. This simple design allows to combine the generalizability from monocular data with the quality of multi-view data. FlexAvatar summary: - Input: Single-image, phone scan, or monocular video - Output: Full 360° head avatar - Expressive animations - Real-time rendering and animation - Generalization to any portrait - Create a new avatar in 2 minutes - Use bias sinks to combine 2D and 3D data 🏠 🌍 🎥 Great work by Tobias Kirschstein and Simon Giebenhain!

Matthias Niessner

96,371 просмотров • 9 месяцев назад

When every second feels cinematic, the action never stops. Created with the power of Seedance 2.5 on NemoVideo A high-intensity cinematic action sequence featuring realistic movement, dynamic camera work, dramatic destruction, and immersive storytelling. #NemoVideo Prompt: TITLE: THE SURVIVAL RUN Create a single-page, premium Hollywood disaster-action storyboard in 16:9 widescreen format with 12 cinematic panels covering one continuous 30-second survival sequence. REFERENCE IMAGE 1 (Image1): Use as the EXACT main character reference. Maintain the same facial identity, hairstyle, beard, body proportions, black shirt, black pants and overall appearance consistently in every panel. No character, face, hairstyle or clothing changes. Dust and debris may gradually appear as the disaster progresses. REFERENCE IMAGE 2 (Image2): Use as the EXACT storyboard design and layout reference. Match its premium cinematic panel arrangement, numbering, timecodes, black caption strips, typography hierarchy, borders and professional Hollywood storyboard presentation. STYLE: Photorealistic live-action Hollywood disaster film, ARRI Alexa 35, anamorphic cinematic lens, realistic Kuala Lumpur, Malaysia, practical stunt realism, believable destruction physics, handheld tracking, cinematic lighting, motion blur, dust, shattered glass, sparks and realistic debris. CORE RULE: ONE CONTINUOUS SURVIVAL SHOT. Every movement must logically connect. No teleportation or unexplained location changes. The same man constantly runs, stumbles, recovers, jumps, slides and changes direction while the collapsing city becomes the antagonist. STORYBOARD PANELS: 1. 00:00–00:03 — EARTHQUAKE Low-angle behind his feet as he runs through Kuala Lumpur. The ground shakes, buildings tremble, glass explodes and concrete crashes behind him. He looks back and accelerates. 2. 00:03–00:06 — COLLAPSE Front tracking shot. A building façade collapses behind him. He changes direction and jumps over a metal barrier as debris destroys his original path. 3. 00:06–00:09 — CAR IMPACT At an intersection, vehicles slide toward him. He dives at the last second, a car passes overhead, he rolls, recovers and keeps running. 4. 00:09–00:12 — ROAD CRACK A huge fissure tears across the road. He runs onto a damaged car as a springboard and leaps across collapsing pavement. 5. 00:12–00:15 — FALLING GLASS Side tracking shot. High-rise windows explode above him. Glass rains down as he shields his face and ducks beneath a falling sign. 6. 00:15–00:18 — COLLAPSE A building collapses ahead. He spots a narrow gap, drops low and slides beneath falling debris, then emerges dusty and runs. 7. 00:18–00:21 — VEHICLE CHAOS A bus crashes into cars. A spinning vehicle approaches. He jumps onto its hood, springs over the roof and lands on the opposite sidewalk. 8. 00:21–00:24 — ELEVATED ROAD FAILURE An elevated road collapses ahead. He performs a desperate full-body gap jump, lands hard, rolls, recovers and runs. 9. 00:24–00:27 — DEBRIS CHASE Heavy dust reduces visibility. Falling streetlights and concrete force sudden direction changes. He stumbles against a wall and pushes forward. 10. 00:27–00:30 — FINAL COLLAPSE Camera faces him as he runs toward it. Massive buildings collapse behind him. He jumps a concrete barrier as a gigantic slab crashes down. 11. FINAL SHOT — STILL RUNNING He emerges through a huge dust cloud, exhausted and covered in debris but clearly recognizable as Reference Image 1, still running toward camera. 12. CUT TO BLACK Black screen with: “THE SURVIVAL RUN” “SOME DISASTERS YOU CAN’T OUTRUN” CAMERA: Low tracking, front pursuit, side tracking, shoulder-mounted chase, whip-pans and ground-level movement. Camera always follows his momentum. No drones, floating cameras or impossible rotations.

When every second feels cinematic, the action never stops. Created with the power of Seedance 2.5 on NemoVideo A high-intensity cinematic action sequence featuring realistic movement, dynamic camera work, dramatic destruction, and immersive storytelling. #NemoVideo Prompt: TITLE: THE SURVIVAL RUN Create a single-page, premium Hollywood disaster-action storyboard in 16:9 widescreen format with 12 cinematic panels covering one continuous 30-second survival sequence. REFERENCE IMAGE 1 (Image1): Use as the EXACT main character reference. Maintain the same facial identity, hairstyle, beard, body proportions, black shirt, black pants and overall appearance consistently in every panel. No character, face, hairstyle or clothing changes. Dust and debris may gradually appear as the disaster progresses. REFERENCE IMAGE 2 (Image2): Use as the EXACT storyboard design and layout reference. Match its premium cinematic panel arrangement, numbering, timecodes, black caption strips, typography hierarchy, borders and professional Hollywood storyboard presentation. STYLE: Photorealistic live-action Hollywood disaster film, ARRI Alexa 35, anamorphic cinematic lens, realistic Kuala Lumpur, Malaysia, practical stunt realism, believable destruction physics, handheld tracking, cinematic lighting, motion blur, dust, shattered glass, sparks and realistic debris. CORE RULE: ONE CONTINUOUS SURVIVAL SHOT. Every movement must logically connect. No teleportation or unexplained location changes. The same man constantly runs, stumbles, recovers, jumps, slides and changes direction while the collapsing city becomes the antagonist. STORYBOARD PANELS: 1. 00:00–00:03 — EARTHQUAKE Low-angle behind his feet as he runs through Kuala Lumpur. The ground shakes, buildings tremble, glass explodes and concrete crashes behind him. He looks back and accelerates. 2. 00:03–00:06 — COLLAPSE Front tracking shot. A building façade collapses behind him. He changes direction and jumps over a metal barrier as debris destroys his original path. 3. 00:06–00:09 — CAR IMPACT At an intersection, vehicles slide toward him. He dives at the last second, a car passes overhead, he rolls, recovers and keeps running. 4. 00:09–00:12 — ROAD CRACK A huge fissure tears across the road. He runs onto a damaged car as a springboard and leaps across collapsing pavement. 5. 00:12–00:15 — FALLING GLASS Side tracking shot. High-rise windows explode above him. Glass rains down as he shields his face and ducks beneath a falling sign. 6. 00:15–00:18 — COLLAPSE A building collapses ahead. He spots a narrow gap, drops low and slides beneath falling debris, then emerges dusty and runs. 7. 00:18–00:21 — VEHICLE CHAOS A bus crashes into cars. A spinning vehicle approaches. He jumps onto its hood, springs over the roof and lands on the opposite sidewalk. 8. 00:21–00:24 — ELEVATED ROAD FAILURE An elevated road collapses ahead. He performs a desperate full-body gap jump, lands hard, rolls, recovers and runs. 9. 00:24–00:27 — DEBRIS CHASE Heavy dust reduces visibility. Falling streetlights and concrete force sudden direction changes. He stumbles against a wall and pushes forward. 10. 00:27–00:30 — FINAL COLLAPSE Camera faces him as he runs toward it. Massive buildings collapse behind him. He jumps a concrete barrier as a gigantic slab crashes down. 11. FINAL SHOT — STILL RUNNING He emerges through a huge dust cloud, exhausted and covered in debris but clearly recognizable as Reference Image 1, still running toward camera. 12. CUT TO BLACK Black screen with: “THE SURVIVAL RUN” “SOME DISASTERS YOU CAN’T OUTRUN” CAMERA: Low tracking, front pursuit, side tracking, shoulder-mounted chase, whip-pans and ground-level movement. Camera always follows his momentum. No drones, floating cameras or impossible rotations.

Zar⭕on

15,357 просмотров • 19 дней назад

AI TENNIS ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, PYTORCH, AND KEYPOINT EXTRACTION. Take any tennis match broadcast, any camera angle, any resolution. Feed it into the pipeline. YOLO detects both players and the tennis ball frame by frame. No manual labeling, no pre-annotated dataset. A fine-tuned YOLOv5 model trained on a Roboflow tennis ball dataset handles the ball - the hardest object to track in any sport. Tiny, fast, constantly occluded. The model finds it anyway. Trackers maintain identity across frames so Player 1 stays Player 1 from the first serve to match point. But detection is just the start. A ResNet50 CNN trained in PyTorch predicts court keypoints from every frame - the corners, service lines, baselines, net posts. Fourteen points that define the entire playing surface geometry. From those keypoints the system builds a homography matrix and warps the broadcast perspective into a top-down mini court with real coordinates. Now every player has a position in real space, not pixel space. Every frame becomes a measurement. Every rally becomes a dataset. Player movement speed - calculated from position deltas between frames, converted to meters per second through the homography. Ball shot speed - measured from the ball trajectory across consecutive detections. Number of shots per rally - counted automatically through ball direction changes. All of this rendered live on the video as an overlay. A mini court in the corner showing both players as dots moving in real time. Stats updating after every point. OpenCV handles the rendering. Pandas handles the math. PyTorch handles the intelligence. YOLO handles the eyes. No Hawkeye subscription, no court-embedded sensors, no tracking chips in the ball. A Python script, a trained model, and a GPU. The full code is on GitHub. The tutorial walks through every module - from ball detector training to court keypoint extraction to the final statistical overlay. Professional teams used to need broadcast deals and proprietary hardware for this kind of analysis. Now you build it in an afternoon with open-source tools. Trading here: Computer vision didn't just enter tennis. It made the expensive stuff free.

AI TENNIS ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, PYTORCH, AND KEYPOINT EXTRACTION. Take any tennis match broadcast, any camera angle, any resolution. Feed it into the pipeline. YOLO detects both players and the tennis ball frame by frame. No manual labeling, no pre-annotated dataset. A fine-tuned YOLOv5 model trained on a Roboflow tennis ball dataset handles the ball - the hardest object to track in any sport. Tiny, fast, constantly occluded. The model finds it anyway. Trackers maintain identity across frames so Player 1 stays Player 1 from the first serve to match point. But detection is just the start. A ResNet50 CNN trained in PyTorch predicts court keypoints from every frame - the corners, service lines, baselines, net posts. Fourteen points that define the entire playing surface geometry. From those keypoints the system builds a homography matrix and warps the broadcast perspective into a top-down mini court with real coordinates. Now every player has a position in real space, not pixel space. Every frame becomes a measurement. Every rally becomes a dataset. Player movement speed - calculated from position deltas between frames, converted to meters per second through the homography. Ball shot speed - measured from the ball trajectory across consecutive detections. Number of shots per rally - counted automatically through ball direction changes. All of this rendered live on the video as an overlay. A mini court in the corner showing both players as dots moving in real time. Stats updating after every point. OpenCV handles the rendering. Pandas handles the math. PyTorch handles the intelligence. YOLO handles the eyes. No Hawkeye subscription, no court-embedded sensors, no tracking chips in the ball. A Python script, a trained model, and a GPU. The full code is on GitHub. The tutorial walks through every module - from ball detector training to court keypoint extraction to the final statistical overlay. Professional teams used to need broadcast deals and proprietary hardware for this kind of analysis. Now you build it in an afternoon with open-source tools. Trading here: Computer vision didn't just enter tennis. It made the expensive stuff free.

zostaff

121,478 просмотров • 5 месяцев назад

#Viggle is back with an open model — Viggle-Animate — and it’s pretty damn capable. For the first time, they’re releasing weights + code. It replaces characters in video using MiniMax H3. Take a video, grab one frame, manually redraw the character as anything you want, and the model propagates that replacement through the entire clip while preserving motion, camera, and timing. No pose skeleton, segmentation, face tracking, depth, or text prompt. Just the video + one modified frame. Under the hood: a 33.1B MiniMax H3 ref2va fine-tune + DMD2 LoRA. Distillation brings it down to 4 sampling steps. It’s 6× faster than Wan2.2-Animate-14B. The catch: hardware. BF16 needs ~62 GiB for the transformer and peaks at ~80.1 GiB VRAM. They recommend 96 GB+ or CPU offload. It reportedly runs on a 32 GB RTX 5090 via NVFP4, but that quant isn’t public yet. Also, you can’t use a normal character portrait. The reference has to be a frame from the original video, already redrawn as the target character. #ai #web3

#Viggle is back with an open model — Viggle-Animate — and it’s pretty damn capable. For the first time, they’re releasing weights + code. It replaces characters in video using MiniMax H3. Take a video, grab one frame, manually redraw the character as anything you want, and the model propagates that replacement through the entire clip while preserving motion, camera, and timing. No pose skeleton, segmentation, face tracking, depth, or text prompt. Just the video + one modified frame. Under the hood: a 33.1B MiniMax H3 ref2va fine-tune + DMD2 LoRA. Distillation brings it down to 4 sampling steps. It’s 6× faster than Wan2.2-Animate-14B. The catch: hardware. BF16 needs ~62 GiB for the transformer and peaks at ~80.1 GiB VRAM. They recommend 96 GB+ or CPU offload. It reportedly runs on a 32 GB RTX 5090 via NVFP4, but that quant isn’t public yet. Also, you can’t use a normal character portrait. The reference has to be a frame from the original video, already redrawn as the target character. #ai #web3

VORTEX: AI Bros & AI Arena, Peak AI Buzz

47,030 просмотров • 20 дней назад

$FAU Erlangen-Nürnberg presents TRIPS Trilinear Point Splatting for Real-Time Radiance Field Rendering paper page: Point-based radiance field rendering has demonstrated impressive results for novel view synthesis, offering a compelling blend of rendering quality and computational efficiency. However, also latest approaches in this domain are not without their shortcomings. 3D Gaussian Splatting [Kerbl and Kopanas et al. 2023] struggles when tasked with rendering highly detailed scenes, due to blurring and cloudy artifacts. On the other hand, ADOP [R\"uckert et al. 2022] can accommodate crisper images, but the neural reconstruction network decreases performance, it grapples with temporal instability and it is unable to effectively address large gaps in the point cloud. In this paper, we present TRIPS (Trilinear Point Splatting), an approach that combines ideas from both Gaussian Splatting and ADOP. The fundamental concept behind our novel technique involves rasterizing points into a screen-space image pyramid, with the selection of the pyramid layer determined by the projected point size. This approach allows rendering arbitrarily large points using a single trilinear write. A lightweight neural network is then used to reconstruct a hole-free image including detail beyond splat resolution. Importantly, our render pipeline is entirely differentiable, allowing for automatic optimization of both point sizes and positions. Our evaluation demonstrate that TRIPS surpasses existing state-of-the-art methods in terms of rendering quality while maintaining a real-time frame rate of 60 frames per second on readily available hardware. This performance extends to challenging scenarios, such as scenes featuring intricate geometry, expansive landscapes, and auto-exposed footage.$

FAU Erlangen-Nürnberg presents TRIPS Trilinear Point Splatting for Real-Time Radiance Field Rendering paper page: Point-based radiance field rendering has demonstrated impressive results for novel view synthesis, offering a compelling blend of rendering quality and computational efficiency. However, also latest approaches in this domain are not without their shortcomings. 3D Gaussian Splatting [Kerbl and Kopanas et al. 2023] struggles when tasked with rendering highly detailed scenes, due to blurring and cloudy artifacts. On the other hand, ADOP [R\"uckert et al. 2022] can accommodate crisper images, but the neural reconstruction network decreases performance, it grapples with temporal instability and it is unable to effectively address large gaps in the point cloud. In this paper, we present TRIPS (Trilinear Point Splatting), an approach that combines ideas from both Gaussian Splatting and ADOP. The fundamental concept behind our novel technique involves rasterizing points into a screen-space image pyramid, with the selection of the pyramid layer determined by the projected point size. This approach allows rendering arbitrarily large points using a single trilinear write. A lightweight neural network is then used to reconstruct a hole-free image including detail beyond splat resolution. Importantly, our render pipeline is entirely differentiable, allowing for automatic optimization of both point sizes and positions. Our evaluation demonstrate that TRIPS surpasses existing state-of-the-art methods in terms of rendering quality while maintaining a real-time frame rate of 60 frames per second on readily available hardware. This performance extends to challenging scenarios, such as scenes featuring intricate geometry, expansive landscapes, and auto-exposed footage.

AK

45,489 просмотров • 2 лет назад

Created this race using GPT Image 2 and Seedance 2.0 on TapNow Prompt Follow the storyboard strictly in exact order from Panel 1 to Panel 9. Do not skip, merge, or rearrange scenes. Keep the SAME female cyclist identity across the entire film. No face changes, no hairstyle changes, no helmet changes, no body proportion inconsistencies. Baby pink must remain the dominant apparel color throughout all cycling scenes. Avoid black wardrobe replacements. Preserve realistic nighttime lighting continuity between shots. Maintain the same cool blue tones and subtle red light reflections. Heavy rain intensity must stay visually consistent across all scenes. Water physics must look physically accurate: droplets, splashes, mist, wheel spray, and runoff should behave naturally. Avoid artificial AI motion. Camera movement should feel like real cinema rigs, FPV drones, mounted bike cameras, or stabilized tracking systems. Drone shots must maintain locked framing and smooth movement without random drifting or orbiting. Use subtle cinematic motion only — no excessive shaking or jitter. Keep realistic breathing, body fatigue, pedaling mechanics, and fabric reactions to wind and rain. Preserve shallow depth of field in macro shots and atmospheric haze in wide shots. Keep the environment dark, moody, and cinematic with strong contrast between wet reflections and darkness. Ensure all reflections on asphalt, water droplets, and bike components react naturally to changing light sources. Maintain premium commercial pacing: slow controlled preparation and macro shots transitioning into aggressive high-speed riding sequences. Final output should resemble a high-budget Nike / Rapha night cycling commercial shot during a real mountain storm. Ultra-realistic cinematic night cycling commercial about female endurance cyclists riding through an intense rainstorm in the mountains at night. Premium Nike / Rapha aesthetic with baby pink performance cycling apparel as the dominant accent color. Hyper-realistic documentary look, no stylization, no anime look, no beauty filters. Natural skin texture, realistic rain interaction, physically accurate water behavior, cinematic low-key lighting, cool blue night tones mixed with subtle red rear-light reflections. Heavy rain, fog, wet asphalt reflections, cinematic motion blur, high dynamic range, shallow depth of field, premium sports commercial quality. The film follows a strict 9-panel storyboard structure with seamless cinematic transitions and continuity preserved across every scene. The SAME female cyclist identity must remain consistent throughout the entire video: same face, helmet, glasses, body proportions, baby pink apparel, lighting style, and overall appearance. Maintain continuity of rain intensity, wetness, fog density, and environmental lighting between all shots. Panel 1: Extreme macro close-up of the female cyclist’s eyes and face in heavy rain at night. Focus on soaked eyelashes, wet skin texture, raindrops streaming across the face, baby pink helmet and baby pink face mask visible. Red rear bike light flickers dynamically across her eyes and skin while cool blue night tones dominate the scene. High contrast cinematic lighting, shallow depth of field, subtle breathing motion, intense determined expression. Panel 2: Cinematic medium close-up frontal shot of the cyclist riding aggressively through heavy rain at night. She pedals hard with strong effort and forward-leaning posture. Baby pink waterproof cycling jacket soaked with rainwater. Front bike light cuts through fog and rain with subtle flickering illumination. Wet asphalt reflects red and white lights. Smooth cinematic tracking shot with controlled stable motion and slight natural float. Panel 3: Ultra-realistic macro shot of large raindrops impacting wet asphalt at night. Crown-shaped splashes and overlapping ripples in slow motion. Rough wet asphalt texture, cool blue cinematic tones, subtle reflections from bike lights.

Created this race using GPT Image 2 and Seedance 2.0 on TapNow Prompt Follow the storyboard strictly in exact order from Panel 1 to Panel 9. Do not skip, merge, or rearrange scenes. Keep the SAME female cyclist identity across the entire film. No face changes, no hairstyle changes, no helmet changes, no body proportion inconsistencies. Baby pink must remain the dominant apparel color throughout all cycling scenes. Avoid black wardrobe replacements. Preserve realistic nighttime lighting continuity between shots. Maintain the same cool blue tones and subtle red light reflections. Heavy rain intensity must stay visually consistent across all scenes. Water physics must look physically accurate: droplets, splashes, mist, wheel spray, and runoff should behave naturally. Avoid artificial AI motion. Camera movement should feel like real cinema rigs, FPV drones, mounted bike cameras, or stabilized tracking systems. Drone shots must maintain locked framing and smooth movement without random drifting or orbiting. Use subtle cinematic motion only — no excessive shaking or jitter. Keep realistic breathing, body fatigue, pedaling mechanics, and fabric reactions to wind and rain. Preserve shallow depth of field in macro shots and atmospheric haze in wide shots. Keep the environment dark, moody, and cinematic with strong contrast between wet reflections and darkness. Ensure all reflections on asphalt, water droplets, and bike components react naturally to changing light sources. Maintain premium commercial pacing: slow controlled preparation and macro shots transitioning into aggressive high-speed riding sequences. Final output should resemble a high-budget Nike / Rapha night cycling commercial shot during a real mountain storm. Ultra-realistic cinematic night cycling commercial about female endurance cyclists riding through an intense rainstorm in the mountains at night. Premium Nike / Rapha aesthetic with baby pink performance cycling apparel as the dominant accent color. Hyper-realistic documentary look, no stylization, no anime look, no beauty filters. Natural skin texture, realistic rain interaction, physically accurate water behavior, cinematic low-key lighting, cool blue night tones mixed with subtle red rear-light reflections. Heavy rain, fog, wet asphalt reflections, cinematic motion blur, high dynamic range, shallow depth of field, premium sports commercial quality. The film follows a strict 9-panel storyboard structure with seamless cinematic transitions and continuity preserved across every scene. The SAME female cyclist identity must remain consistent throughout the entire video: same face, helmet, glasses, body proportions, baby pink apparel, lighting style, and overall appearance. Maintain continuity of rain intensity, wetness, fog density, and environmental lighting between all shots. Panel 1: Extreme macro close-up of the female cyclist’s eyes and face in heavy rain at night. Focus on soaked eyelashes, wet skin texture, raindrops streaming across the face, baby pink helmet and baby pink face mask visible. Red rear bike light flickers dynamically across her eyes and skin while cool blue night tones dominate the scene. High contrast cinematic lighting, shallow depth of field, subtle breathing motion, intense determined expression. Panel 2: Cinematic medium close-up frontal shot of the cyclist riding aggressively through heavy rain at night. She pedals hard with strong effort and forward-leaning posture. Baby pink waterproof cycling jacket soaked with rainwater. Front bike light cuts through fog and rain with subtle flickering illumination. Wet asphalt reflects red and white lights. Smooth cinematic tracking shot with controlled stable motion and slight natural float. Panel 3: Ultra-realistic macro shot of large raindrops impacting wet asphalt at night. Crown-shaped splashes and overlapping ripples in slow motion. Rough wet asphalt texture, cool blue cinematic tones, subtle reflections from bike lights.

Sharon Riley

72,716 просмотров • 4 месяцев назад

The broken heart cat is now getting stronger and is ready to fight for his life. Seedance 2.5 on TapNow Prompt A cinematic 30-second action sequence in a misty Japanese wetland. An orange tabby cat with a white chest, white paws, and intense green-gold eyes wears a traditional woven straw conical kasa hat and a small katana strapped diagonally across its back with a leather harness. A thin stalk of dry reed hangs from its mouth like a toothpick. Overcast gray sky, dense tall beige reeds, moss-covered rocks, damp earth, light fog, muted earthy palette, filmic grain, shallow depth of field, motion blur on fast movement. Opening close-up: the cat sits in profile on a mossy rock, hat low over its eyes, looking off to the side. It slowly turns its head to face the camera with a calm, knowing stare. Cut to a tracking shot as the cat walks then sprints toward camera from behind a large mossy boulder, hat bouncing, tail up, reeds parting. Two black-clad ninjas in full face-covering outfits appear in the reeds. The cat weaves between them at high speed, sword flashing, kicking up dirt and leaves. Low-angle ground-level shots with heavy motion blur as the cat dashes past. The cat drops low and sprints through a dense bamboo-like reed tunnel, hat almost covering its face, then bursts out and leaps high into the air, body stretched, sword on its back, spinning against the gray sky. Mid-air flips and corkscrew jumps through tall swaying reeds. One shot from below as the cat silhouettes against the overcast sky. Another as it twists and lands rolling on the wet grass. A ninja swings a katana; the cat dodges in mid-air, hat flying slightly off-center. Quick cuts of the cat running, sliding, leaping again. The cat soars high one last time and lands on the bow of a small dark wooden rowboat floating on a still, misty lake. Ripples spread. Several ninjas leap from the reed bank toward the boat in dramatic slow-motion, swords drawn, bodies mid-jump. The cat lands in a low, wide stance on the wet wooden planks, front paws planted, staring straight into the camera. Hat slightly tilted. It then walks slowly and confidently forward along the center of the boat toward the lens, sword still on its back, expression unreadable and slightly menacing. Background reeds and fogged water. Distant ninjas splash or fall behind it. Cinematic camera work throughout: mix of locked-off portraits, low tracking shots, handheld chase energy, dramatic low angles, overhead flips, and a final slow push-in on the cat’s face. Natural overcast lighting, soft fog, wet surfaces, photorealistic fur and fabric texture, 24fps film look, slightly desaturated, epic yet slightly absurd tone. No dialogue, only wind, rustling reeds, splashes, and distant sword sounds.

Sharon Riley

47,024 просмотров • 2 дней назад

Prompt: Create a 43-second cinematic, photorealistic action-comedy sequence inside an old, cramped, slightly abandoned apartment/bathroom with realistic worn walls, tiled surfaces, warm practical lighting, and natural shadows. A young adult man in a light gray T-shirt is standing in front of a bathroom mirror. Suddenly, another man wearing a black-and-white striped shirt appears and aggressively grabs him. The two struggle physically through the small bathroom, with realistic body movement, pushing, pulling, stumbling and fighting. The action continues dynamically through the bathroom. One character crashes against the wall and breaks through a damaged section, creating a large opening into the adjacent room. Dust and small debris fall naturally as the wall breaks. Continue with a fast-paced chase through the apartment. The gray-shirted man runs toward a window while the striped-shirted man follows. Use handheld camera movement, realistic motion blur, quick tracking shots and dramatic close-ups to make the sequence feel like a real action movie. The characters then escape outside into an old brick courtyard/alley. One character climbs onto a rusty metal fire escape while the other follows from below. Show realistic physics, footsteps, clothing movement and environmental interaction. Final scene: transition to a large outdoor dumpster filled with cardboard and garbage. The gray-shirted man is inside the dumpster, looking exhausted and confused, holding a small green plush toy. He slowly looks around as the camera pushes in toward his face. End on a slightly comedic reaction shot. Visual style: ultra-photorealistic, cinematic action film, realistic human anatomy, natural skin texture, detailed environments, practical lighting, subtle film grain, realistic physics, believable facial expressions. Camera: dynamic handheld cinematography, wide establishing shots, medium tracking shots, close-ups during the struggle, smooth camera transitions, natural depth of field, 35mm/50mm cinematic lenses. Motion: realistic human movement, accurate weight and momentum, natural collisions, realistic debris and dust simulation, no slow-motion unless used very briefly for dramatic impact. Important: Maintain consistent character appearance, clothing, hairstyle and body proportions throughout the entire video. No random character changes, no extra limbs, no distorted faces, no cartoon appearance, no text, no subtitles, no watermark.

Prompt: Create a 43-second cinematic, photorealistic action-comedy sequence inside an old, cramped, slightly abandoned apartment/bathroom with realistic worn walls, tiled surfaces, warm practical lighting, and natural shadows. A young adult man in a light gray T-shirt is standing in front of a bathroom mirror. Suddenly, another man wearing a black-and-white striped shirt appears and aggressively grabs him. The two struggle physically through the small bathroom, with realistic body movement, pushing, pulling, stumbling and fighting. The action continues dynamically through the bathroom. One character crashes against the wall and breaks through a damaged section, creating a large opening into the adjacent room. Dust and small debris fall naturally as the wall breaks. Continue with a fast-paced chase through the apartment. The gray-shirted man runs toward a window while the striped-shirted man follows. Use handheld camera movement, realistic motion blur, quick tracking shots and dramatic close-ups to make the sequence feel like a real action movie. The characters then escape outside into an old brick courtyard/alley. One character climbs onto a rusty metal fire escape while the other follows from below. Show realistic physics, footsteps, clothing movement and environmental interaction. Final scene: transition to a large outdoor dumpster filled with cardboard and garbage. The gray-shirted man is inside the dumpster, looking exhausted and confused, holding a small green plush toy. He slowly looks around as the camera pushes in toward his face. End on a slightly comedic reaction shot. Visual style: ultra-photorealistic, cinematic action film, realistic human anatomy, natural skin texture, detailed environments, practical lighting, subtle film grain, realistic physics, believable facial expressions. Camera: dynamic handheld cinematography, wide establishing shots, medium tracking shots, close-ups during the struggle, smooth camera transitions, natural depth of field, 35mm/50mm cinematic lenses. Motion: realistic human movement, accurate weight and momentum, natural collisions, realistic debris and dust simulation, no slow-motion unless used very briefly for dramatic impact. Important: Maintain consistent character appearance, clothing, hairstyle and body proportions throughout the entire video. No random character changes, no extra limbs, no distorted faces, no cartoon appearance, no text, no subtitles, no watermark.

Karlos

25,977 просмотров • 1 месяц назад

Everyone is sleeping on Meta's SAM 3 release. But it's actually a big deal. Here's why: Companies spend millions paying humans to label images and videos frame by frame. A single autonomous driving dataset? Months of work, hundreds of annotators, millions in cost. Without labeled data, you can't train custom models. Without custom models, you're stuck with generic solutions. This is why most companies never move past pilots. SAM 3 breaks this cycle. First let's look at the evolution: SAM 1 segmented objects when you clicked on them. Revolutionary, but one object at a time. SAM 2 added video tracking with memory. Game-changing, but you still manually prompted every object. SAM 3 changes everything with text prompts. Type "yellow school bus" and it finds ALL of them in your image or video. Not just one. Every instance across thousands of frames. Now here's where people get confused: "Can't I just use GPT-5 or Gemini for this?" No, and here's why that's a terrible approach. Large multimodal LLMs are great for reasoning, but they're slow and expensive for production visual tasks. You're paying API costs per image, waiting seconds for responses, getting inconsistent results. SAM 3 runs in 30 milliseconds on a single GPU for 100+ objects. That's 100x faster, and you own the infrastructure. More importantly, SAM 3 gives you precise pixel-level masks, not descriptions. Try asking an LLM to segment every defective part on a manufacturing line in real-time. It won't work. SAM 3 does this effortlessly. The real breakthrough is their data engine. Meta built an AI-human hybrid system that's 5x faster for complex annotations. They trained SAM 3 on 4 million unique visual concepts - 50x more than existing benchmarks like LVIS. SAM 3 is trained on 4 million unique visual concepts, it handles everything: - Text-based concept search - Interactive refinement with clicks - Video tracking across frames - Zero-shot detection of new concepts The model is open source. Weights, code, and benchmarks are on GitHub. If you're building computer vision applications, this is the foundation model to evaluate. The annotation time savings alone will pay for integration costs within weeks. Find the relevant links in the next tweet!

Everyone is sleeping on Meta's SAM 3 release. But it's actually a big deal. Here's why: Companies spend millions paying humans to label images and videos frame by frame. A single autonomous driving dataset? Months of work, hundreds of annotators, millions in cost. Without labeled data, you can't train custom models. Without custom models, you're stuck with generic solutions. This is why most companies never move past pilots. SAM 3 breaks this cycle. First let's look at the evolution: SAM 1 segmented objects when you clicked on them. Revolutionary, but one object at a time. SAM 2 added video tracking with memory. Game-changing, but you still manually prompted every object. SAM 3 changes everything with text prompts. Type "yellow school bus" and it finds ALL of them in your image or video. Not just one. Every instance across thousands of frames. Now here's where people get confused: "Can't I just use GPT-5 or Gemini for this?" No, and here's why that's a terrible approach. Large multimodal LLMs are great for reasoning, but they're slow and expensive for production visual tasks. You're paying API costs per image, waiting seconds for responses, getting inconsistent results. SAM 3 runs in 30 milliseconds on a single GPU for 100+ objects. That's 100x faster, and you own the infrastructure. More importantly, SAM 3 gives you precise pixel-level masks, not descriptions. Try asking an LLM to segment every defective part on a manufacturing line in real-time. It won't work. SAM 3 does this effortlessly. The real breakthrough is their data engine. Meta built an AI-human hybrid system that's 5x faster for complex annotations. They trained SAM 3 on 4 million unique visual concepts - 50x more than existing benchmarks like LVIS. SAM 3 is trained on 4 million unique visual concepts, it handles everything: - Text-based concept search - Interactive refinement with clicks - Video tracking across frames - Zero-shot detection of new concepts The model is open source. Weights, code, and benchmarks are on GitHub. If you're building computer vision applications, this is the foundation model to evaluate. The annotation time savings alone will pay for integration costs within weeks. Find the relevant links in the next tweet!

Akshay 🚀

46,438 просмотров • 10 месяцев назад