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3D Human Pose Estimation From Multi Person Stereo 360 Scenes
AbstractThis 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.
Related Material
[pdf][bibtex]@InProceedings{Shere_2019_CVPR_Workshops,
author = {Shere, Matthew and Kim, Hansung and Hilton, Adrian},
title = {3D Human Pose Estimation From Multi Person Stereo 360 Scenes},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}
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