Model-based Deep Hand Pose Estimation

@inproceedings{Zhou2016ModelbasedDH,
  title={Model-based Deep Hand Pose Estimation},
  author={Xingyi Zhou and Qingfu Wan and Wei Zhang and Xiangyang Xue and Yichen Wei},
  booktitle={IJCAI},
  year={2016}
}
Figure 1: Illustration of model based deep hand pose learning. After standard convolutional layers and fully connected layers, the hand model pose parameters (mostly joint angles) are produced. A new hand model layer maps the pose parameters to the hand joint locations via a forward kinematic process. The joint location loss and a physical constraint based loss guide the end-to-end learning of the network. 
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