Kevin Lin, Lijuan Wang, Zicheng Liu
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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 (GC-NNs) 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 prespecified 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 stateof-the-art methods on multiple benchmarks, including Hu-man3.6M, 3DPW, and FreiHAND datasets. Code and pre-trained models are available at https://github. com/microsoft/MeshGraphormer.
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