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Data-driven models of 3D avatars and clothing for virtual try-on

dc.contributor.authorSantesteban Garay, Igor
dc.descriptionTesis Doctoral leída en la Universidad Rey Juan Carlos de Madrid en 2022. Directores de la Tesis: Dan Casas Guix y Miguel A. Otaduy Tristán Programa de Doctorado en Tecnologías de la Información y las Comunicacioneses
dc.description.abstractClothing plays a fundamental role in our everyday lives. When we choose clothing to buy or wear, we guide our decisions based on a combination of fit and style. For this reason, the majority of clothing is purchased at brick-and-mortar retail stores, after physical try-on to test the fit and style of several garments on our own bodies. Computer graphics technology promises an opportunity to support online shopping through virtual try-on, but to date virtual try-on solutions lack the responsiveness of a physical try-on experience. This thesis works towards developing new virtual try-on solutions that meet the demanding requirements of accuracy, interactivity and scalability. To this end, we propose novel datadriven models for 3D avatars and clothing that produce highly realistic results at a fraction of the computational cost of physics-based approaches. Throughout the thesis we also address common limitations of data-driven methods by using self-supervision mechanisms to enforce physical constraints and reduce the dependency on ground-truth data. This allows us to build efficient and accurate models with minimal preprocessing
dc.publisherUniversidad Rey Juan Carloses
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.subjectmodels of 3Des
dc.titleData-driven models of 3D avatars and clothing for virtual try-ones

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Attribution-NonCommercial-NoDerivatives 4.0 InternacionalExcept where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional