171 citations · 275 across the 9 of their papers we have counts for
9 papers
Polar Interpolants for Thin-Shell Microstructure Homogenization
Antoine Chan-Lock, Miguel Otaduy
This paper introduces a new formulation for material homogenization of thin-shell microstructures. It addresses important challenges that limit the quality of previous approaches:…
FLSH -- Friendly Library for the Simulation of Humans
Pablo Ramón, Cristian Romero, Javier Tapia +1
Computer models of humans are ubiquitous throughout computer animation and computer vision. However, these models rarely represent the dynamics of human motion, as this requires ad…
High-Order Elasticity Interpolants for Microstructure Simulation
Antoine Chan-Lock, Jesus Perez, Miguel Otaduy
We propose a novel formulation of elastic materials based on high-order interpolants, which fits accurately complex elastic behaviors, but remains conservative. The proposed high-o…
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…
RGB2Hands: Real-Time Tracking of 3D Hand Interactions from Monocular RGB Video
Jiayi Wang, Franziska Mueller, Florian Bernard +6
Tracking and reconstructing the 3D pose and geometry of two hands in interaction is a challenging problem that has a high relevance for several human-computer interaction applicati…
Real-time Pose and Shape Reconstruction of Two Interacting Hands With a Single Depth Camera
Franziska Mueller, Micah Davis, Florian Bernard +5
We present a novel method for real-time pose and shape reconstruction of two strongly interacting hands. Our approach is the first two-hand tracking solution that combines an exten…
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…
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…