We present FITE, a First-Implicit-Then-Explicit framework for modeling human avatars in clothing. Our framework first learns implicit surface templates representing the coarse clothing topology, and then employs the templates to guide the generation of point sets which further capture pose-dependent clothing deformations such as wrinkles. Our pipeline incorporates the merits of both implicit and explicit representations, namely, the ability to handle varying topology and the ability to efficiently capture fine details. We also propose diffused skinning to facilitate template training especially for loose clothing, and projection-based pose-encoding to extract pose information from mesh templates without predefined UV map or connectivity. Our code is publicly available at https://github.com/jsnln/fite.

该论文介绍了FITE框架，通过学习隐式表面模板来表示粗糙的衣服拓扑，然后使用模板指导点集生成以捕捉皱褶等姿态依赖性衣服变形。该框架结合了隐式和显式表示的优点，能够处理不同的拓扑结构和有效地捕捉细节。此外，还提出了扩散蒙皮技术来进行模板训练以处理宽松的衣物，并提出基于投影的姿态编码方法，从网格模板中提取姿态信息。

点云衣着人体建模的学习隐式模板