We present a novel solution to the garment animation problem through deep
learning. Our contribution allows animating any template outfit with arbitrary
topology and geometric complexity. Recent works develop models for garment
edition, resizing and animation at the same time by leveraging the support body
model (encoding garments as body homotopies). This leads to complex engineering
solutions that suffer from scalability, applicability and compatibility. By
limiting our scope to garment animation only, we are able to propose a simple
model that can animate any outfit, independently of its topology, vertex order
or connectivity. Our proposed architecture maps outfits to animated 3D models
into the standard format for 3D animation (blend weights and blend shapes
matrices), automatically providing of compatibility with any graphics engine.
We also propose a methodology to complement supervised learning with an
unsupervised physically based learning that implicitly solves collisions and
enhances cloth quality.

本研究通过深度学习提出了一种解决服装动画问题的新方法，可以对任何拓扑和几何复杂度的模板服装进行动画处理，并提出了一种补充有监督学习的无监督物理学习方法，以隐式解决碰撞和增强布料质量。