Data-driven models of 3D avatars and clothing for virtual try-on
Clothing 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.
Tesis 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 Comunicaciones
- Tesis Doctorales