In this research, we address the challenge faced by existing deep
learning-based human mesh reconstruction methods in balancing accuracy and
computational efficiency. These methods typically prioritize accuracy,
resulting in large network sizes and excessive computational complexity, which
may hinder their practical application in real-world scenarios, such as virtual
reality systems. To address this issue, we introduce a modular multi-stage
lightweight graph-based transformer network for human pose and shape estimation
from 2D human pose, a pose-based human mesh reconstruction approach that
prioritizes computational efficiency without sacrificing reconstruction
accuracy. Our method consists of a 2D-to-3D lifter module that utilizes graph
transformers to analyze structured and implicit joint correlations in 2D human
poses, and a mesh regression module that combines the extracted pose features
with a mesh template to produce the final human mesh parameters.

本文提出了一种基于图表的变形网络，实现了从二维人体姿态到三维人体姿态的估计和到三维人体网格参数的回归。该方法在保证精度的同时，优化了计算效率，具有广泛的实际应用价值。