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

dc.contributor.authorSantesteban, Igor
dc.contributor.authorThuerey, Nils
dc.contributor.authorOtaduy, Miguel A.
dc.contributor.authorCasas, Dan
dc.identifier.citationGMRV 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 movieses
dc.description.abstractWe 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
dc.publisherGMRV Publicationses
dc.rightsAtribución-NoComercial-CompartirIgual 4.0 Internacional*
dc.titleSelf-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-Ones
dc.subject.unesco1203.17 Informáticaes

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Atribución-NoComercial-CompartirIgual 4.0 InternacionalExcept where otherwise noted, this item's license is described as Atribución-NoComercial-CompartirIgual 4.0 Internacional