Model-Based Deep Hand Pose Estimation
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 2421-2426.
Abstract
Previous learning based hand pose estimation methods does not fully exploit the prior information in hand model geometry. Instead, they usually rely a separate model fitting step to generate valid hand poses. Such a post processing is inconvenient and sub-optimal. In this work, we propose a model based deep learning approach that adopts a forward kinematics based layer to ensure the geometric validity of estimated poses. For the first time, we show that embedding such a non-linear generative process in deep learning is feasible for hand pose estimation. Our approach is verified on challenging public datasets and achieves state-of-the-art performance.
@inproceedings{ijcai2016p345,
title = {Model-Based Deep Hand Pose Estimation},
author = {Zhou, Xingyi and Wan, Qingfu and Zhang, Wei and Xue, Xiangyang and Wei, Yichen},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {2421--2426},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/345},
}
TY - CPAPER TI - Model-Based Deep Hand Pose Estimation AU - Zhou, Xingyi AU - Wan, Qingfu AU - Zhang, Wei AU - Xue, Xiangyang AU - Wei, Yichen T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 2421 EP - 2426 UR - https://ijcai.org/proceedings/2016/345 ER -Copied
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