Graphrpe: Relative Position Encoding Graph Transformer for 3d Human Pose Estimation

The graph neural network has been playing increasingly important roles in 2D-3D lifting based single-frame human pose estimation. However, it still suffers from inferior modeling of local and global associations between 2D nodes. To this end, we introduce two relative position encoding approaches and propose a novel graph-transformer structure "GraphRPE." The model consists of two components: graph relative position encoding (GRPE) and universal relative position encoding (URPE). GRPE embeds graph structure prior information into the attention map to correct the self-attention weights and prompt appropriate interactions between 2D nodes, both locally and globally. On the other hand, URPE introduces the Toeplitz matrix to address the limited representation capability of the transformer. In addition, we investigate several graph edge types and their impacts on the results. Extensive experimental results demonstrate that our proposed method achieves SOTA performance on the Human3.6M dataset.