  Recent approaches to drape garments quickly over arbitrary human bodies
leverage self-supervision to eliminate the need for large training sets.
However, they are designed to train one network per clothing item, which
severely limits their generalization abilities. In our work, we rely on
self-supervision to train a single network to drape multiple garments. This is
achieved by predicting a 3D deformation field conditioned on the latent codes
of a generative network, which models garments as unsigned distance fields. Our
pipeline can generate and drape previously unseen garments of any topology,
whose shape can be edited by manipulating their latent codes. Being fully
differentiable, our formulation makes it possible to recover accurate 3D models
of garments from partial observations -- images or 3D scans -- via gradient
descent. Our code is publicly available at
https://github.com/liren2515/DrapeNet .
