18 citations · 35 across the 7 of their papers we have counts for
15 papers
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019
Zhengying Liu, Adrien Pavao, Zhen Xu +22
This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series, which helped sorting out a profusion of AutoML solutions for Deep Learning (DL) th…
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…
ChaLearn Looking at People: Inpainting and Denoising challenges
Sergio Escalera, Marti Soler, Stephane Ayache +6
Dealing with incomplete information is a well studied problem in the context of machine learning and computational intelligence. However, in the context of computer vision, the pro…
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…