Abstract
Fabien Baradel, Romain Brégier, Thibault Groueix, Philippe Weinzaepfel, Yannis Kalantidis, Gregory Rogez |
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), IEEE, Special issue on Transformer Models in Vision, 12 December, 2022 |
arXiv |
IEEE |
@ARTICLE{9982410, author={Baradel, Fabien and Brégier, Romain and Groueix, Thibault and Weinzaepfel, Philippe and Kalantidis, Yannis and Rogez, Grégory}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, title={PoseBERT: A Generic Transformer Module for Temporal 3D Human Modeling}, year={2022}, volume={}, number={}, pages={1-17}, doi={10.1109/TPAMI.2022.3216899}}
Abstract
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 been recently utilized for body pose sequence modeling, related methods rely on pseudo-ground truth to augment the currently limited training data available for learning such models. In this paper, we introduce PoseBERT, a transformer module that is fully trained on 3D Motion Capture (MoCap) data via masked modeling. It is simple, generic and versatile, as it can be plugged on top of any image-based model to transform it in a video-based model leveraging temporal information. We showcase variants of PoseBERT with different inputs varying from 3D skeleton keypoints to rotations of a 3D parametric model for either the full body (SMPL) or just the hands (MANO). Since PoseBERT training is task agnostic, the model can be applied to several tasks such as pose refinement, future pose prediction or motion completion without finetuning. Our experimental results validate that adding PoseBERT on top of various state-of-the-art pose estimation methods consistently improves their performances, while its low computational cost allows us to use it in a real-time demo for smoothly animating a robotic hand via a webcam.
Details on the gender equality index score 2023 (related to year 2022) for NAVER France of 81/100.
NAVER France targets are as follows:
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Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2023 au titre des données 2022 : 81/100
Détail des indicateurs :
Les objectifs de progression de NAVER France sont :
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