  We present a graph-convolution-reinforced transformer, named Mesh Graphormer,
for 3D human pose and mesh reconstruction from a single image. Recently both
transformers and graph convolutional neural networks (GCNNs) have shown
promising progress in human mesh reconstruction. Transformer-based approaches
are effective in modeling non-local interactions among 3D mesh vertices and
body joints, whereas GCNNs are good at exploiting neighborhood vertex
interactions based on a pre-specified mesh topology. In this paper, we study
how to combine graph convolutions and self-attentions in a transformer to model
both local and global interactions. Experimental results show that our proposed
method, Mesh Graphormer, significantly outperforms the previous
state-of-the-art methods on multiple benchmarks, including Human3.6M, 3DPW, and
FreiHAND datasets
