CVPR 2023 - DrapeNet: Garment Generation and Self-Supervised Draping

In this episode we discuss DrapeNet: Garment Generation and Self-Supervised Draping by Authors: - Luca De Luigi - Ren Li - Benoît Guillard - Mathieu Salzmann - Pascal Fua Affiliations: - Luca De Luigi: University of Bologna, luca.deluigi4@unibo.it - Ren Li, Benoît Guillard, Mathieu Salzmann, Pascal Fua: CVLab, EPFL, {name.surname}@epfl.ch. The paper presents a new approach to drape garments over human bodies using self-supervision to train a single network for multiple garments instead of one network per clothing item. The network predicts a 3D deformation field based on the latent codes of a generative network that models garments as unsigned distance fields. The approach enables the generation and draping of previously unseen garments with different topologies, which can be edited by manipulating their latent codes. The fully differentiable formulation also allows for accurate 3D modeling of garments from partial observations. The code is publicly available.