197 citations · 256 across the 9 of their papers we have counts for
9 papers · 1 filter
Purposer: Putting Human Motion Generation in Context
Nicolas Ugrinovic, Thomas Lucas, Fabien Baradel +3
We present a novel method to generate human motion to populate 3D indoor scenes. It can be controlled with various combinations of conditioning signals such as a path in a scene, t…
SHOWMe: Benchmarking Object-agnostic Hand-Object 3D Reconstruction
Anilkumar Swamy, Vincent Leroy, Philippe Weinzaepfel +6
Recent hand-object interaction datasets show limited real object variability and rely on fitting the MANO parametric model to obtain groundtruth hand shapes. To go beyond these lim…
4DHumanOutfit: a multi-subject 4D dataset of human motion sequences in varying outfits exhibiting large displacements
Matthieu Armando, Laurence Boissieux, Edmond Boyer +11
This work presents 4DHumanOutfit, a new dataset of densely sampled spatio-temporal 4D human motion data of different actors, outfits and motions. The dataset is designed to contain…
Reliability in Semantic Segmentation: Are We on the Right Track?
Pau de Jorge, Riccardo Volpi, Philip Torr +1
Motivated by the increasing popularity of transformers in computer vision, in recent times there has been a rapid development of novel architectures. While in-domain performance fo…
PoseBERT: A Generic Transformer Module for Temporal 3D Human Modeling
Fabien Baradel, Romain Brégier, Thibault Groueix +3
Training state-of-the-art models for human pose estimation in videos requires datasets with annotations that are really hard and expensive to obtain. Although transformers have bee…
Barely-Supervised Learning: Semi-Supervised Learning with very few labeled images
Thomas Lucas, Philippe Weinzaepfel, Gregory Rogez
This paper tackles the problem of semi-supervised learning when the set of labeled samples is limited to a small number of images per class, typically less than 10, problem that we…