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.

翻译：针对大规模拥挤场景中因相互遮挡、严重尺度变化及复杂空间分布导致的多人网格恢复方法无法生成精确绝对身体姿态与形状的问题，我们充分挖掘人群特征实现单目图像中群体重建。提出一种新型超图关系推理网络，以建模人群中个体与群体间复杂的高阶关系关联。首先从原始高分辨率图像中提取紧凑人体特征与位置信息，通过对提取的个体特征进行关系推理，利用潜在的人群集体性与交互关系为重建提供额外群体信息，最终结合更新后的个体特征与定位信息回归相机坐标系下的人体网格。为促进网络训练，进一步构建两个人群数据集的伪真值，这将推动拥挤场景下姿态估计与人类行为理解的后续研究。实验结果表明，本方法在拥挤与常规场景中均优于其他基线方法。代码与数据集已公开于 https://github.com/boycehbz/GroupRec。