  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.
