A Lightweight Graph Transformer Network for Human Mesh Reconstruction from 2D Human Pose

Ce Zheng, Matias Mendieta, Pu Wang, Chen Chen, Aidong Lu

Abstract:

Existing deep learning-based human mesh reconstruction approaches have a tendency to build larger networks to achieve higher accuracy.
Computational complexity and model size are often neglected, despite being key characteristics for practical use of human mesh
reconstruction models (e.g. virtual try-on systems). In this paper, we present GTRS, a lightweight pose-based method that can reconstruct human mesh from 2D human pose. We propose a pose analysis module that uses graph transformers to exploit structured
and implicit joint correlations, and a mesh regression module that combines the extracted pose feature with the mesh template to reconstruct the final human mesh. We demonstrate the efficiency and generalization of GTRS by extensive evaluations on the Human3.6M and 3DPW datasets. In particular, GTRS achieves better accuracy than the SOTA pose-based method Pose2Mesh while only
using 10.2% of the parameters (Params) and 2.5% of the FLOPs on the challenging in-the-wild 3DPW dataset. Code is available at
https://github.com/zczcwh/GTRS