Summary of Dreamwaltz-g: Expressive 3d Gaussian Avatars From Skeleton-guided 2d Diffusion, by Yukun Huang et al.
DreamWaltz-G: Expressive 3D Gaussian Avatars from Skeleton-Guided 2D Diffusion
by Yukun Huang, Jianan Wang, Ailing Zeng, Zheng-Jun Zha, Lei Zhang, Xihui Liu
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); Machine Learning (cs.LG)
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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 Recent advancements in text-to-3D avatar generation have leveraged 2D diffusion models and score distillation sampling (SDS) for promising results. However, generating high-quality avatars capable of expressive animation remains a challenge. DreamWaltz-G, a novel learning framework, addresses this issue by integrating skeleton controls from 3D human templates into 2D diffusion models via Skeleton-guided Score Distillation. This enhances consistency in view and human pose, mitigating issues like multiple faces, extra limbs, and blurring. The proposed hybrid 3D Gaussian avatar representation combines neural implicit fields and parameterized 3D meshes for real-time rendering, stable SDS optimization, and expressive animation. DreamWaltz-G outperforms existing methods in both visual quality and animation expressiveness, supporting diverse applications like human video reenactment and multi-subject scene composition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating 3D avatars that can move around and look realistic. Right now, it’s hard to make these avatars look good while also being able to control them. The researchers created a new way to make these avatars using a combination of computer vision and machine learning techniques. This new method is called DreamWaltz-G. It helps create 3D avatars that are more realistic and can be controlled in different ways. The results show that this new method works better than previous methods, which means it could be used to make movies or video games with lifelike characters. |
Keywords
» Artificial intelligence » Diffusion » Distillation » Machine learning » Optimization
