Reconstructing Groups of People with Hypergraph Relational Reasoning

TL;DR

This paper introduces a hypergraph relational reasoning network that leverages crowd features and high-order relations to improve multi-person mesh recovery in crowded scenes from monocular images.

Contribution

It proposes a novel hypergraph-based model that captures complex relations among individuals and groups, enhancing accuracy in crowded scene pose estimation.

Findings

Outperforms baseline methods in crowded scenarios

Effective in reconstructing accurate human meshes

Utilizes pseudo ground-truth for training enhancement

Abstract

Due to the mutual occlusion, severe scale variation, and complex spatial distribution, the current multi-person mesh recovery methods cannot produce accurate absolute body poses and shapes in large-scale crowded scenes. To address the obstacles, we fully exploit crowd features for reconstructing groups of people from a monocular image. A novel hypergraph relational reasoning network is proposed to formulate the complex and high-order relation correlations among individuals and groups in the crowd. We first extract compact human features and location information from the original high-resolution image. By conducting the relational reasoning on the extracted individual features, the underlying crowd collectiveness and interaction relationship can provide additional group information for the reconstruction. Finally, the updated individual features and the localization information are used to regress human meshes in camera coordinates. To facilitate the network training, we further build pseudo ground-truth on two crowd datasets, which may also promote future research on pose estimation and human behavior understanding in crowded scenes. The experimental results show that our approach outperforms other baseline methods both in crowded and common scenarios. The code and datasets are publicly available at https://github.com/boycehbz/GroupRec.