Summary of Amg: Avatar Motion Guided Video Generation, by Zhangsihao Yang et al.
AMG: Avatar Motion Guided Video Generation
by Zhangsihao Yang, Mengyi Shan, Mohammad Farazi, Wenhui Zhu, Yanxi Chen, Xuanzhao Dong, Yalin Wang
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed AMG method is a deep generative model that combines the photorealism of 2D media generation with the controllability of 3D avatar-based approaches. It conditions video diffusion models on controlled rendering of 3D avatars, allowing for multi-person diffusion video generation with precise control over camera positions, human motions, and background style. The method outperforms existing human video generation methods in terms of realism and adaptability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AMG is a new way to make realistic videos by combining the best parts of 2D and 3D methods. It lets you control things like camera angles, character movements, and backgrounds. This makes it really good at making videos that look like real life. The method also does better than other ways to make human-like videos. |
Keywords
» Artificial intelligence » Diffusion » Generative model
