Pre-weighted Modulated Dense Graph Convolutional Networks for 3D Human Pose Estimation

Abstract

Graph convolutional networks (GCN) have increasingly become one of the main research hotspots in 3D human pose estimation. The method of modeling the relationship between human joint points by GCN has achieved good performance in 3D human pose estimation. However, the 3D human pose estimation method based on GCN has issues of over-smooth and indistinguishable importance between joint points and adjacent joint points. To address these issues, this paper designs a modulated dense connection (MDC) module and a pre-weighted graph convolutional module, and proposes a pre-weighted modulated dense graph convolutional network (WMDGCN) for 3D human pose estimation based on these two modules. For the problem of over-smoothing, the modulation dense connection can better realize feature reuse through hyperparameter [α] and [β] (hyperparameter [α] represents the weight proportion of features of layer L to previous layers, and hyperparameter [β] represents the propagation strategies of the features of previous layers to layer L), thus effectively improving the expression ability of features. To address the issue of not distinguishing the importance of the joint points and adjacent joint points, the pre-weighted graph convolution is used to assign higher weights to the joint point. Different weight matrices are used for the joint point and its adjacent joint points to capture human joint point features more effectively. Comparative experimental results on the Human3.6M dataset show that the proposed method achieves the best performance in terms of parameter number and performance. The parameter number, MPJPE and P-MPJPE values of WMDGCN are 0.27 MB, 37.46 mm and 28.85 mm, respectively.

Keywords

• 3d human pose estimation; graph convolution network; pre-weighted