@inproceedings{lin2021mesh,
author = {Lin, Kevin and Wang, Lijuan and Liu, Zicheng},
title = {Mesh Graphormer},
booktitle = {ICCV 2021},
year = {2021},
month = {October},
abstract = {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 on GitHub.},
url = {http://approjects.co.za/?big=en-us/research/publication/mesh-graphormer/},
}