Camera Distance-Aware Top-Down Approach for 3D Multi-Person Pose Estimation from a Single RGB Image
Abstract
Although significant improvement has been achieved recently in 3D human pose estimation, most of the previous methods only treat a single-person case. In this work, we firstly propose a fully learning-based, camera distance-aware top-down approach for 3D multi-person pose estimation from a single RGB image. The pipeline of the proposed system consists of human detection, absolute 3D human root localization, and root-relative 3D single-person pose estimation modules. Our system achieves comparable results with the state-of-the-art 3D single-person pose estimation models without any groundtruth information and significantly outperforms previous 3D multi-person pose estimation methods on publicly available datasets. The code is available in (https://github.com/mks0601/3DMPPE_ROOTNET_RELEASE) , (https://github.com/mks0601/3DMPPE_POSENET_RELEASE).
Cite
Text
Moon et al. "Camera Distance-Aware Top-Down Approach for 3D Multi-Person Pose Estimation from a Single RGB Image." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019. doi:10.1109/ICCV.2019.01023Markdown
[Moon et al. "Camera Distance-Aware Top-Down Approach for 3D Multi-Person Pose Estimation from a Single RGB Image." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.](https://mlanthology.org/iccv/2019/moon2019iccv-camera/) doi:10.1109/ICCV.2019.01023BibTeX
@inproceedings{moon2019iccv-camera,
title = {{Camera Distance-Aware Top-Down Approach for 3D Multi-Person Pose Estimation from a Single RGB Image}},
author = {Moon, Gyeongsik and Chang, Ju Yong and Lee, Kyoung Mu},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
year = {2019},
doi = {10.1109/ICCV.2019.01023},
url = {https://mlanthology.org/iccv/2019/moon2019iccv-camera/}
}