Mesh Graphormer

Code

• github.com/microsoft/meshgraphormer
  OfficialIn papernone★ 425
• github.com/MS-P3/code5/tree/main/graphormer
  mindspore★ 0
• github.com/2024-MindSpore-1/Code2/tree/main/model-1/graphormer
  mindspore★ 0

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 at https://github.com/microsoft/MeshGraphormer

Tasks

Benchmark Results

Reproductions