关键词: 手姿态估计, 高分辨率表示, 多尺度融合, 视角不变, 深度学习

Abstract:

At present, most of the networks used for 2D keypoint heatmaps estimation of hand pose use the convolutional pose machines or Hourglass network, but these two networks cannot simultaneously satisfy the requirements of high-resolution representation preserving learning and multi-scale feature fusion. In response to this problem, a multi-scale high-resolution preserving network is used, which adopts the structure of high-resolution and low-resolution representation in parallel design, and enhances the features of each resolution through the fusion of all resolution representations, and has multiple stages to extract high quality features for 2D heatmaps estimation. In order to obtain the 3D hand pose, a global rotation perspective-invariant method is also used to map the 2D heatmaps to the 3D pose. Experiments on 2D hand pose estimation and 3D hand pose estimation are conducted on three public datasets（RHD, STB, Dexter+Object）, and the results verify the effectiveness of the method in hand pose estimation.

Key words: hand pose estimation, high-resolution representation, multi-scale fusion, perspective-invariant, deep learning