IJCAI Conference 2016 Conference Paper
Model-Based Deep Hand Pose Estimation
- Xingyi Zhou
- Qingfu Wan
- Wei Zhang
- Xiangyang Xue
- Yichen Wei
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.