4 citations · 11 across the 3 of their papers we have counts for
5 papers
SNUG: Self-Supervised Neural Dynamic Garments
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. State-of-the-art data-driven approaches to model 3D garment deform…
Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On
Igor Santesteban, Nils Thuerey, Miguel A. Otaduy +1
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 garme…
Fully Convolutional Graph Neural Networks for Parametric Virtual Try-On
Raquel Vidaurre, Igor Santesteban, Elena Garces +1
We present a learning-based approach for virtual try-on applications based on a fully convolutional graph neural network. In contrast to existing data-driven models, which are trai…
SoftSMPL: Data-driven Modeling of Nonlinear Soft-tissue Dynamics for Parametric Humans
Igor Santesteban, Elena Garces, Miguel A. Otaduy +1
We present SoftSMPL, a learning-based method to model realistic soft-tissue dynamics as a function of body shape and motion. Datasets to learn such task are scarce and expensive to…
Learning-Based Animation of Clothing for Virtual Try-On
Igor Santesteban, Miguel A. Otaduy, Dan Casas
This paper presents a learning-based clothing animation method for highly efficient virtual try-on simulation. Given a garment, we preprocess a rich database of physically-based dr…