Mesh Graphormer

Kevin Lin, Lijuan Wang, Zicheng Liu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 12939-12948


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. Code and pre-trained models are available at

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@InProceedings{Lin_2021_ICCV, author = {Lin, Kevin and Wang, Lijuan and Liu, Zicheng}, title = {Mesh Graphormer}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {12939-12948} }