Ayuda
Ir al contenido

Dialnet


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

  • Autores: Igor Santesteban Garay
  • Directores de la Tesis: Dan Casas Guix (dir. tes.), Miguel Ángel Otaduy Tristan (codir. tes.)
  • Lectura: En la Universidad Rey Juan Carlos ( España ) en 2022
  • Idioma: español
  • Tribunal Calificador de la Tesis: Niloy Mitra (presid.), Antonio García Marqués (secret.), Justus Thies (voc.)
  • Programa de doctorado: Programa de Doctorado en Tecnologías de la Información y las Comunicaciones por la Universidad Rey Juan Carlos
  • Materias:
  • Texto completo no disponible (Saber más ...)
  • Resumen
    • 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 data-driven 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.


Fundación Dialnet

Dialnet Plus

  • Más información sobre Dialnet Plus

Opciones de compartir

Opciones de entorno