Santesteban, IgorThuerey, NilsOtaduy, Miguel A.Casas, Dan2021-10-262021-10-262021GMRV Publications Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On Igor Santesteban, Nils Thürey, Miguel A. Otaduy, Dan Casas IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) - 2021 Images and movieshttp://hdl.handle.net/10115/18251We 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.engAtribución-NoComercial-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-sa/4.0/Informáticainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccess1203.17 Informática