1 code implementation • CVPR 2022 • Igor Santesteban, Miguel A. Otaduy, Dan Casas
We present a self-supervised method to learn dynamic 3D deformations of garments worn by parametric human bodies.
no code implementations • 22 Jun 2021 • Jiayi Wang, Franziska Mueller, Florian Bernard, Suzanne Sorli, Oleksandr Sotnychenko, Neng Qian, Miguel A. Otaduy, Dan Casas, Christian Theobalt
Moreover, we demonstrate that our approach offers previously unseen two-hand tracking performance from RGB, and quantitatively and qualitatively outperforms existing RGB-based methods that were not explicitly designed for two-hand interactions.
no code implementations • 15 Jun 2021 • Franziska Mueller, Micah Davis, Florian Bernard, Oleksandr Sotnychenko, Mickeal Verschoor, Miguel A. Otaduy, Dan Casas, Christian Theobalt
We present a novel method for real-time pose and shape reconstruction of two strongly interacting hands.
1 code implementation • CVPR 2021 • Igor Santesteban, Nils Thuerey, Miguel A. Otaduy, Dan Casas
We 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.
no code implementations • 1 Apr 2020 • Igor Santesteban, Elena Garces, Miguel A. Otaduy, Dan Casas
We present SoftSMPL, a learning-based method to model realistic soft-tissue dynamics as a function of body shape and motion.
1 code implementation • 17 Mar 2019 • Igor Santesteban, Miguel A. Otaduy, Dan Casas
We propose a model that separates global garment fit, due to body shape, from local garment wrinkles, due to both pose dynamics and body shape.