Summary of Anigs: Animatable Gaussian Avatar From a Single Image with Inconsistent Gaussian Reconstruction, by Lingteng Qiu et al.
AniGS: Animatable Gaussian Avatar from a Single Image with Inconsistent Gaussian Reconstruction
by Lingteng Qiu, Shenhao Zhu, Qi Zuo, Xiaodong Gu, Yuan Dong, Junfei Zhang, Chao Xu, Zhe Li, Weihao Yuan, Liefeng Bo, Guanying Chen, Zilong Dong
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper tackles the challenge of generating animatable human avatars from a single image, crucial for digital human modeling applications. Existing methods struggle to capture fine details or suffer from viewpoint inconsistencies. The authors propose a two-part solution: first, they generate multi-view canonical pose images using a transformer-based video generation model, pre-trained on a large-scale video dataset. Then, they develop an efficient 3D reconstruction method using 4D Gaussian Splatting to handle view inconsistencies. The approach enables photorealistic, real-time animation of human avatars from in-the-wild images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create realistic and moving human figures for digital modeling. Right now, it’s hard to make these figures look good because we don’t have enough information. The researchers found a way to fix this by making pictures that can be viewed from different angles, which helps them figure out the 3D shape of the person. They also developed a new method to reconstruct the 3D model quickly and accurately. This is important because it means we can make realistic animations fast. |
Keywords
» Artificial intelligence » Transformer
