MocapNET: Ensemble of SNN Encoders for 3D Human Pose Estimation in RGB Images
Ammar Qammaz (CSD-UOC and ICS-FORTH), Antonis Argyros (CSD-UOC and ICS-FORTH) AbstractWe present MocapNET, an ensemble of SNN encoders that estimates the 3D human body pose based on 2D joint estimations extracted from monocular RGB images. MocapNET provides an efficient divide and conquer strategy for supervised learning. It outputs skeletal information directly into the BVH format which can be rendered in real-time or imported without any additional processing in most popular 3D animation software. The proposed architecture achieves 3D human pose estimations at state of the art rates of 400Hz using only CPU processing.
DOI
10.5244/C.33.143
https://dx.doi.org/10.5244/C.33.143
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@inproceedings{BMVC2019,
title={MocapNET: Ensemble of SNN Encoders for 3D Human Pose Estimation in RGB Images},
author={Ammar Qammaz and Antonis Argyros},
year={2019},
month={September},
pages={143.1--143.17},
articleno={143},
numpages={17},
booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
publisher={BMVA Press},
editor={Kirill Sidorov and Yulia Hicks},
doi={10.5244/C.33.143},
url={https://dx.doi.org/10.5244/C.33.143}
}
title={MocapNET: Ensemble of SNN Encoders for 3D Human Pose Estimation in RGB Images},
author={Ammar Qammaz and Antonis Argyros},
year={2019},
month={September},
pages={143.1--143.17},
articleno={143},
numpages={17},
booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
publisher={BMVA Press},
editor={Kirill Sidorov and Yulia Hicks},
doi={10.5244/C.33.143},
url={https://dx.doi.org/10.5244/C.33.143}
}