Demo of XNect: Real-time Multi-person 3D Human
Pose Estimation with a Single RGB Camera
Update: XNect Demo(V2) Presented at CVPR 2019
Abstract
We present a real-time multi-person 3D human body pose estimation system which makes use of a single RGB camera for human motion capture in general scenes. Our learning based approach gives full body 3D articulation estimates even under strong partial occlusion, as well as estimates of camera relative localization in space. Our approach makes use of the detected 2D body joint locations as well as the joint detection confidence values, and is trained using our recently proposed Multi-person Composited 3D Human Pose (MuCo-3DHP) dataset, and also leverages MS-COCO person keypoints dataset for improved performance in general scenes. Our system can handle an arbitrary number of people in the scene, and processes complete frames without requiring prior person detection.
Real-time Demo Examples
Citation
@inproceedings{XNectDemo_ECCV2018,
TITLE = {Demo of {XNect}: Real-time Multi-person {3D} Human Pose Estimation with a Single {RGB} Camera},
AUTHOR = {Mehta, Dushyant and Sotnychenko, Oleksandr and Mueller, Franziska and Rhodin, Helge and Xu, Weipeng and Pons-Moll, Gerard and Theobalt, Christian},
LANGUAGE = {eng},
URL = {http://gvv.mpi-inf.mpg.de/projects/XNectDemo/},
YEAR = {2018},
MARGINALMARK = {$\bullet$},
BOOKTITLE = {ECCV 2018 Demo Sessions},
ADDRESS = {Munich, Germany},
}
@inproceedings{Mehta_3DV2018,
TITLE = {Single-Shot Multi-person {3D} Pose Estimation from Monocular {RGB}},
AUTHOR = {Mehta, Dushyant and Sotnychenko, Oleksandr and Mueller, Franziska and Xu, Weipeng and Sridhar, Srinath and Pons-Moll, Gerard and Theobalt, Christian},
LANGUAGE = {eng},
ISBN = {978-1-5386-8425-2 ; 978-1-5386-8426-9},
DOI = {10.1109/3DV.2018.00024},
PUBLISHER = {IEEE},
YEAR = {2018},
MARGINALMARK = {$\bullet$},
DATE = {2018},
BOOKTITLE = {3DV 2018 , International Conference on 3D Vision},
PAGES = {120--130},
ADDRESS = {Verona, Italy},
}