论文标题
学习基于点衣服的人类建模的隐式模板
Learning Implicit Templates for Point-Based Clothed Human Modeling
论文作者
论文摘要
我们提出了Fite，这是一个前图 - 这是对衣服中人体化身进行建模的解释框架。我们的框架首先学习了代表粗衣拓扑的隐式表面模板，然后采用模板来指导点集的产生，从而进一步捕获姿势依赖的服装变形，例如皱纹。我们的管道结合了隐式和明确表示的优点，即处理变化拓扑的能力以及有效捕获细节的能力。我们还提出了扩散的皮肤，以促进模板训练，尤其是用于宽松衣服的训练，以及基于投影的姿势编码，以从网格模板中提取姿势信息，而没有预定义的紫外线图或连接性。我们的代码可在https://github.com/jsnln/fite上公开获取。
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.
