  Modern multi-layer perceptron (MLP) models have shown competitive results in
learning visual representations without self-attention. However, existing MLP
models are not good at capturing local details and lack prior knowledge of
human body configurations, which limits their modeling power for skeletal
representation learning. To address these issues, we propose a simple yet
effective graph-reinforced MLP-Like architecture, named GraphMLP, that combines
MLPs and graph convolutional networks (GCNs) in a global-local-graphical
unified architecture for 3D human pose estimation. GraphMLP incorporates the
graph structure of human bodies into an MLP model to meet the domain-specific
demand of the 3D human pose, while allowing for both local and global spatial
interactions. Furthermore, we propose to flexibly and efficiently extend the
GraphMLP to the video domain and show that complex temporal dynamics can be
effectively modeled in a simple way with negligible computational cost gains in
the sequence length. To the best of our knowledge, this is the first MLP-Like
architecture for 3D human pose estimation in a single frame and a video
sequence. Extensive experiments show that the proposed GraphMLP achieves
state-of-the-art performance on two datasets, i.e., Human3.6M and MPI-INF-3DHP.
Code and models are available at https://github.com/Vegetebird/GraphMLP.
