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.

本文提出了一种基于姿势的轻量级方法 GTRS，它可以从 2D 人类姿态重构人类网格。该方法利用图变换器来提取结构化和隐式的关节相关性以及网格模板来重构人类网格，从而大大提高了模型的效率和泛化性能。在 Human3.6M 和 3DPW 数据集上进行广泛的评估，GTRS 方法在仅使用 10.2％ 的参数和 2.5％ 的 FLOPs 的同时，比 SOTA 基于姿态的 Pose2Mesh 方法实现了更好的准确性。

一个轻量级的图变换网络用于从2D人体姿势重建人体网格