18 citations · 18 across the 3 of their papers we have counts for
13 papers
Leveraging MoCap Data for Human Mesh Recovery
Fabien Baradel, Thibault Groueix, Philippe Weinzaepfel +3
Training state-of-the-art models for human body pose and shape recovery from images or videos requires datasets with corresponding annotations that are really hard and expensive to…
SMPLy Benchmarking 3D Human Pose Estimation in the Wild
Vincent Leroy, Philippe Weinzaepfel, Romain Brégier +2
Predicting 3D human pose from images has seen great recent improvements. Novel approaches that can even predict both pose and shape from a single input image have been introduced,…
Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning
Riccardo Volpi, Diane Larlus, Grégory Rogez
Most standard learning approaches lead to fragile models which are prone to drift when sequentially trained on samples of a different nature - the well-known "catastrophic forgetti…
DOPE: Distillation Of Part Experts for whole-body 3D pose estimation in the wild
Philippe Weinzaepfel, Romain Brégier, Hadrien Combaluzier +2
We introduce DOPE, the first method to detect and estimate whole-body 3D human poses, including bodies, hands and faces, in the wild. Achieving this level of details is key for a n…
Progressive Skeletonization: Trimming more fat from a network at initialization
Pau de Jorge, Amartya Sanyal, Harkirat S. Behl +3
Recent studies have shown that skeletonization (pruning parameters) of networks \textit{at initialization} provides all the practical benefits of sparsity both at inference and tra…
Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation under Hand-Object Interaction
Anil Armagan, Guillermo Garcia-Hernando, Seungryul Baek +32
We study how well different types of approaches generalise in the task of 3D hand pose estimation under single hand scenarios and hand-object interaction. We show that the accuracy…