Chaoyang Wang

Researcher working on 4D generative AI

[Jiraphon Yenphraphai @JYenphraphai]

🧬 [1/3] Videos → dynamic 3D meshes 🧬

Excited to share Helix4D, our framework for complex 4D mesh generation from video!

It handles hard 4D cases: topology changes, inner surfaces, transparency, deformation, and thin structures.

👉 Project: https://t.co/xu0UZp5EID

[Jiraphon Yenphraphai @JYenphraphai]

[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 👉 https://t.co/YH1UtEYfHP

[Ashkan Mirzaei @ashmrz10]

[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. 👇

https://t.co/vr9Wnvfx0s

[Hsin-Ying Lee @hyjameslee]

🔥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!
👑https://t.co/nVQeP5LNLO

[Hsin-Ying Lee @hyjameslee]

📢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!
https://t.co/aFF9Ut93LK

[AK @_akhaliq]

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,