# A Modular Multi-stage Lightweight Graph Transformer Network for Human Pose and Shape Estimation from 2D Human Pose

**Authors:** Ayman Ali, Ekkasit Pinyoanuntapong, Pu Wang, Mohsen Dorodchi  
**arXiv:** [2301.13403](https://arxiv.org/abs/2301.13403)  
**Date:** 2023-01-31  
**Summary page:** https://www.summarizepaper.com/en/paper/2301.13403v1/

## Key points

- Existing deep learning-based methods for human mesh reconstruction face challenges related to large network sizes and excessive computational complexity
- Introduction of a modular multi-stage lightweight graph-based transformer network prioritizes computational efficiency without compromising on reconstruction accuracy
- Approach consists of two main modules: 2D-to-3D lifter module and mesh regression module
- 2D-to-3D lifter module utilizes graph transformers to analyze joint correlations in 2D human poses, aiming to improve accuracy and robustness by separating the learning of human pose, shape, and camera parameters
- Mesh regression module combines pose features with a mesh template to generate final human mesh parameters
- Challenges include depth ambiguity, complex backgrounds, and diverse human poses when recovering human meshes from images without additional devices like depth sensors
- Goal is to design an end-to-end capable graph-based transformer network that accurately estimates human shape and pose parameters while demonstrating performance comparable to state-of-the-art methods
- Proposed approach aims to enhance efficiency and effectiveness of human mesh reconstruction through a modular multi-stage pipeline and separate learning strategies for different parameters

## Summary

In this research, we address the challenge faced by existing deep learning-based methods for human mesh reconstruction. The traditional approaches prioritize accuracy but suffer from large network sizes and excessive computational complexity. To overcome this limitation, we introduce a modular multi-stage lightweight graph-based transformer network that prioritizes computational efficiency without compromising on reconstruction accuracy. Our approach consists of two main modules: a 2D-to-3D lifter module and a mesh regression module. The 2D-to-3D lifter module utilizes graph transformers to analyze structured and implicit joint correlations in 2D human poses. By separating the learning of human pose, shape, and camera parameters, our model aims to improve accuracy and robustness. The mesh regression module combines pose features with a mesh template to generate the final human mesh parameters. This task presents challenges such as depth ambiguity, complex backgrounds, and diverse human poses when recovering human meshes from images without additional devices like depth sensors. Previous methods have focused on minimizing 2D reprojection loss or iteratively optimizing pose and shape parameters from images using parametric models like SMPL. Our goal is to design an end-to-end capable graph-based transformer network that accurately estimates human shape and pose parameters while demonstrating performance comparable to state-of-the-art methods. By implementing a modular multi-stage pipeline and leveraging separate learning strategies for different parameters, our proposed approach aims to enhance the efficiency and effectiveness of human mesh reconstruction in various applications.