Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On

• URL: http://arxiv.org/abs/2105.06462v1
• Date: Thu, 13 May 2021 17:58:20 GMT
• Title: Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On
• Authors: Igor Santesteban, Nils Thuerey, Miguel A. Otaduy, Dan Casas
• Abstract summary: We propose a new generative model for 3D garment deformations that enables us to learn, for the first time, a data-driven method for virtual try-on. We show that our method is the first to successfully address garment-body contact in unseen body shapes and motions, without compromising realism and detail.
• Score: 29.458328272854107
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We propose a new generative model for 3D garment deformations that enables us to learn, for the first time, a data-driven method for virtual try-on that effectively addresses garment-body collisions. In contrast to existing methods that require an undesirable postprocessing step to fix garment-body interpenetrations at test time, our approach directly outputs 3D garment configurations that do not collide with the underlying body. Key to our success is a new canonical space for garments that removes pose-and-shape deformations already captured by a new diffused human body model, which extrapolates body surface properties such as skinning weights and blendshapes to any 3D point. We leverage this representation to train a generative model with a novel self-supervised collision term that learns to reliably solve garment-body interpenetrations. We extensively evaluate and compare our results with recently proposed data-driven methods, and show that our method is the first to successfully address garment-body contact in unseen body shapes and motions, without compromising realism and detail.

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