---
title: "Model-Based Deep Hand Pose Estimation"
authors:
  - "Xingyi Zhou"
  - "Qingfu Wan"
  - "Wei Zhang"
  - "Xiangyang Xue"
  - "Yichen Wei"
doi: ""
url: "https://ijcai.org/proceedings/2016/345"
pdf: "https://ijcai.org/proceedings/2016/0345.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "2421-2426"
track: ""
keywords:
---

# Model-Based Deep Hand Pose Estimation

Xingyi Zhou, Qingfu Wan, Wei Zhang, Xiangyang Xue, Yichen Wei

*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.