Chaoyang Wang

[1/3] 🚀 Introducing ShapeGen4D: video → high-quality 4D mesh sequences. A native, end-to-end video-to-4D model that turns monocular videos into high-quality mesh sequences (no per-frame optimization). details 👉 shapegen4d.github.io

[1/9] 🚀 We introduce 4Real-Video-V2, a method that can generate 4D scenes from a simple text prompt, viewable from any angle at any moment in time. It’s fast, photorealistic, and works on full scenes. Here's how it works and why it matters. 👇 snap-research.github.io/4Real-Video-V2/

🔥DELTA: Dense Efficient Long-range 3D Tracking for any video DELTA can efficiently (10x faster!) track EVERY pixel in 3D space from monocular videos. Please check out our project page and paper for more details and samples! 👑snap-research.github.io/DELTA/

📢4Real-Video: Learning Generalizable Photo-Realistic 4D Video Diffusion Do you want to explore space-time traversal? Do you want to convert any real-world images/videos to 4D? Check out our recent 4Real-Video! snap-research.github.io/4Real-Video/

4Real Towards Photorealistic 4D Scene Generation via Video Diffusion Models Existing dynamic scene generation methods mostly rely on distilling knowledge from pre-trained 3D generative models, which are typically fine-tuned on synthetic object datasets. As a result,