Santesteban Garay, Igor2022-12-022022-12-022022https://hdl.handle.net/10115/20698Tesis 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 ComunicacionesClothing 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 times.engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/models of 3Davatarinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/embargoedAccess