3D Human Pose Estimation from Multi Person Stereo 360 Scenes
Abstract
This paper presents a human tracking and 3D pose estimation algorithm for use with a pair of 360 cameras. We identify and track an individual throughout complex, multi-person scenes in both indoor and outdoor environments using appearance models and positional data, and produce a temporally consistent 3D skeleton by optimising a skeleton of realistic joint lengths over joint positions produce by Convolutional Pose Machines (CPMs). Our results show an average improvement of 22.67% over state of the art deep learning approaches for tracking, as well as reasonable estimates for pose using just two cameras.
Cite
Text
Shere et al. "3D Human Pose Estimation from Multi Person Stereo 360 Scenes." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.Markdown
[Shere et al. "3D Human Pose Estimation from Multi Person Stereo 360 Scenes." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/shere2019cvprw-3d/)BibTeX
@inproceedings{shere2019cvprw-3d,
title = {{3D Human Pose Estimation from Multi Person Stereo 360 Scenes}},
author = {Shere, Matthew and Kim, Hansung and Hilton, Adrian},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2019},
pages = {1-8},
url = {https://mlanthology.org/cvprw/2019/shere2019cvprw-3d/}
}