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.
@article{arxiv.1606.06854,
title = {Model-based Deep Hand Pose Estimation},
author = {Xingyi Zhou and Qingfu Wan and Wei Zhang and Xiangyang Xue and Yichen Wei},
journal= {arXiv preprint arXiv:1606.06854},
year = {2016}
}