VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment
Abstract
We present mph{VoxelPose} to estimate $3$D poses of multiple people from multiple camera views. In contrast to the previous efforts which require to establish cross-view correspondence based on noisy and incomplete $2$D pose estimates, mph{VoxelPose} directly operates in the $3$D space therefore avoids making incorrect decisions in each camera view. To achieve this goal, features in all camera views are aggregated in the $3$D voxel space and fed into mph{Cuboid Proposal Network} (CPN) to localize all people. Then we propose mph{Pose Regression Network} (PRN) to estimate a detailed $3$D pose for each proposal. The approach is robust to occlusion which occurs frequently in practice. Without bells and whistles, it outperforms the previous methods on several public datasets.
Cite
Text
Tu et al. "VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment." Proceedings of the European Conference on Computer Vision (ECCV), 2020. doi:10.1007/978-3-030-58452-8_12Markdown
[Tu et al. "VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment." Proceedings of the European Conference on Computer Vision (ECCV), 2020.](https://mlanthology.org/eccv/2020/tu2020eccv-voxelpose/) doi:10.1007/978-3-030-58452-8_12BibTeX
@inproceedings{tu2020eccv-voxelpose,
title = {{VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment}},
author = {Tu, Hanyue and Wang, Chunyu and Zeng, Wenjun},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2020},
doi = {10.1007/978-3-030-58452-8_12},
url = {https://mlanthology.org/eccv/2020/tu2020eccv-voxelpose/}
}