从单目视频重建可分离衣物的3D avatar，支持自由编辑。
GGAvatar: Reconstructing Garment-Separated 3D Gaussian Splatting Avatars from Monocular Video
- 用参数化模板和分阶段训练，实现衣物与身体解耦建模。
- 相比昂贵模型，在质量和效率上均表现更优。
- 适合虚拟试衣、角色动画等需要衣物编辑的场景。
Avatar建模在人体动画和虚拟试衣中应用广泛。近期研究多关注高质量、全面的人体重建，但常忽略衣物与身体的分离。本文提出GGAvatar（Garment-separated 3D Gaussian Splatting Avatar），基于单目视频，通过先进的参数化模板和独特的分阶段训练，有效实现了衣物与身体的解耦、可编辑且逼真的重建。与其它高成本模型的对比评估表明，GGAvatar在建模有衣物人体及可分离衣物方面具有更优的质量与效率。论文还展示了衣物编辑的应用实例，如图1所示，凸显了有效解耦的优势。代码已开源：https://github.com/J-X-Chen/GGAvatar/。
原文摘要 · Abstract (English)
Avatar modelling has broad applications in human animation and virtual try-ons. Recent advancements in this field have focused on high-quality and comprehensive human reconstruction but often overlook the separation of clothing from the body. To bridge this gap, this paper introduces GGAvatar (Garment-separated 3D Gaussian Splatting Avatar), which relies on monocular videos. Through advanced parameterized templates and unique phased training, this model effectively achieves decoupled, editable, and realistic reconstruction of clothed humans. Comparative evaluations with other costly models confirm GGAvatar's superior quality and efficiency in modelling both clothed humans and separable garments. The paper also showcases applications in clothing editing, as illustrated in Figure 1, highlighting the model's benefits and the advantages of effective disentanglement. The code is available at https://github.com/J-X-Chen/GGAvatar/.
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