  Existing deep learning-based human mesh reconstruction approaches have a
tendency to build larger networks in order 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 will be publicly available.
