10 citations · 11 across the 2 of their papers we have counts for
6 papers
Neural Implicit Surfaces for Efficient and Accurate Collisions in Physically Based Simulations
Hugo Bertiche, Meysam Madadi, Sergio Escalera
Current trends in the computer graphics community propose leveraging the massive parallel computational power of GPUs to accelerate physically based simulations. Collision detectio…
Deep unsupervised 3D human body reconstruction from a sparse set of landmarks
Meysam Madadi, Hugo Bertiche, Sergio Escalera
In this paper we propose the first deep unsupervised approach in human body reconstruction to estimate body surface from a sparse set of landmarks, so called DeepMurf. We apply a d…
PBNS: Physically Based Neural Simulator for Unsupervised Garment Pose Space Deformation
Hugo Bertiche, Meysam Madadi, Sergio Escalera
We present a methodology to automatically obtain Pose Space Deformation (PSD) basis for rigged garments through deep learning. Classical approaches rely on Physically Based Simulat…
DeePSD: Automatic Deep Skinning And Pose Space Deformation For 3D Garment Animation
Hugo Bertiche, Meysam Madadi, Emilio Tylson +1
We present a novel solution to the garment animation problem through deep learning. Our contribution allows animating any template outfit with arbitrary topology and geometric comp…
CLOTH3D: Clothed 3D Humans
Hugo Bertiche, Meysam Madadi, Sergio Escalera
This work presents CLOTH3D, the first big scale synthetic dataset of 3D clothed human sequences. CLOTH3D contains a large variability on garment type, topology, shape, size, tightn…
SMPLR: Deep SMPL reverse for 3D human pose and shape recovery
Meysam Madadi, Hugo Bertiche, Sergio Escalera
Current state-of-the-art in 3D human pose and shape recovery relies on deep neural networks and statistical morphable body models, such as the Skinned Multi-Person Linear model (SM…