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arXiv · 2002.02033

Rotation-invariant Mixed Graphical Model Network for 2D Hand Pose Estimation

Deying Kong·Haoyu Ma·Yifei Chen·Xiaohui Xie
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Abstract

In this paper, we propose a new architecture named Rotation-invariant Mixed Graphical Model Network (R-MGMN) to solve the problem of 2D hand pose estimation from a monocular RGB image. By integrating a rotation net, the R-MGMN is invariant to rotations of the hand in the image. It also has a pool of graphical models, from which a combination of graphical models could be selected, conditioning on the input image. Belief propagation is performed on each graphical model separately, generating a set of marginal distributions, which are taken as the confidence maps of hand keypoint positions. Final confidence maps are obtained by aggregating these confidence maps together. We evaluate the R-MGMN on two public hand pose datasets. Experiment results show our model outperforms the state-of-the-art algorithm which is widely used in 2D hand pose estimation by a noticeable margin.

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Deying Kong, Haoyu Ma, Yifei Chen, Xiaohui Xie. 2020-02-05. Rotation-invariant Mixed Graphical Model Network for 2D Hand Pose Estimation. https://arxiv.org/abs/2002.02033

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