MetaPose: Fast 3D Pose from Multiple Views without 3D Supervision
Ben Usman Andrea Tagliasacchi Kate Saenko Avneesh Sud
Boston University Google Research Simon Fraser University MIT-IBM Watson AI Lab
In CVPR 2022
Paper | Code | Demo Videos
MetaPose accurately estimates 3D human poses, takes into account multi-view uncertainty, and uses only 2D supervision for training! It is faster and more accurate, especially with fewer cameras.
Abstract
In the era of deep learning, human pose estimation from multiple cameras with unknown calibration has received little attention to date. We show how to train a neural model to perform this task with high precision and minimal latency overhead. The proposed model takes into account joint location uncertainty due to occlusion from multiple views, and requires only 2D keypoint data for training. Our method outperforms both classical bundle adjustment and weakly-supervised monocular 3D baselines on the well-established Human3.6M dataset, as well as the more challenging in-the-wild Ski-Pose PTZ dataset.
Citation
@inproceedings{usman2021metapose,
author = {Usman, Ben and Tagliasacchi, Andrea and Saenko, Kate and Sud, Avneesh},
title = {MetaPose: Fast 3D Pose from Multiple Views without 3D Supervision},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022}
}