GGAvatar：从单目视频重建服装分离的3D高斯泼染化身
GGAvatar: Reconstructing Garment-Separated 3D Gaussian Splatting Avatars from Monocular Video
AI总结：
本文提出GGAvatar，基于单目视频和参数化模板及分阶段训练，实现穿衣人体服装与身体解耦的高质量、可编辑3D高斯泼染化身重建，在质量与效率上优于高成本模型并支持服装编辑。
摘要：
化身建模在人体动画和虚拟试衣中有广泛应用。该领域近期进展集中于高质量、全面的人体重建，但常忽略服装与身体的分离。为弥补此不足，本文提出GGAvatar（服装分离的3D高斯泼染化身），依赖单目视频。通过先进的参数化模板和独特的分阶段训练，该模型有效实现了穿衣人体的解耦、可编辑且逼真的重建。与其他高成本模型的对比评估证实了GGAvatar在建模穿衣人体和可分离服装方面的卓越质量与效率。本文还展示了服装编辑应用（如图1所示），突显了模型优势及有效解耦的益处。代码发布于https://github.com/J-X-Chen/GGAvatar/。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
