Summary of Ggavatar: Reconstructing Garment-separated 3d Gaussian Splatting Avatars From Monocular Video, by Jingxuan Chen
GGAvatar: Reconstructing Garment-Separated 3D Gaussian Splatting Avatars from Monocular Video
by Jingxuan Chen
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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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 presents GGAvatar, a novel approach to avatar modeling that addresses the long-standing issue of separating clothing from the human body. By leveraging monocular videos and advanced parameterized templates, GGAvatar achieves decoupled, editable, and realistic reconstruction of clothed humans. The model’s unique phased training scheme enables efficient processing, outperforming other costly methods in both quality and speed. Applications in clothing editing are showcased, highlighting the benefits of effective disentanglement. The paper also includes comparative evaluations with existing models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GGAvatar is a new way to create digital humans that can wear different clothes. Usually, computer models mix up the person’s body with their clothes, making it hard to change outfits. GGAvatar fixes this problem by using special templates and training techniques that let it separate clothing from the human body. This makes it easier to edit clothes and create new fashion ideas. |
