GraphMLP: A graph MLP-like architecture for 3D human pose estimation

Wenhao Li, Mengyuan Liu, Hong Liu, Tianyu Guo, Ti Wang, Hao Tang, Nicu Sebe

Abstract: Highlights•We present, to the best of our knowledge, the first MLP-Like architecture called GraphMLP for 3D human pose estimation. It combines the advantages of modern MLPs and GCNs, including globality, locality, and connectivity.•The novel SG-MLP and CG-MLP blocks are proposed to encode the graph structure of human bodies within MLPs to obtain domain-specific knowledge about the human body while enabling the model to capture both local and global interactions.•A simple and efficient video representation is further proposed to extend our GraphMLP to the video domain flexibly. This representation enables the model to effectively process arbitrary-length sequences with negligible computational cost gains.