Remarkable progress has been made in 3D human pose estimation from a monocular RGB camera. However, only a few studies explored 3D multi-person cases. In this paper, we attempt to address the lack of a global perspective of the top-down approaches by introducing a novel form of supervision - Hierarchical Multi-person Ordinal Relations (HMOR). The HMOR encodes interaction information as the ordinal relations of depths and angles hierarchically, which captures the body-part and joint level semantic and maintains global consistency at the same time. In our approach, an integrated top-down model is designed to leverage these ordinal relations in the learning process. The integrated model estimates human bounding boxes, human depths, and root-relative 3D poses simultaneously, with a coarse-to-fine architecture to improve the accuracy of depth estimation. The proposed method significantly outperforms state-of-the-art methods on publicly available multi-person 3D pose datasets. In addition to superior performance, our method costs lower computation complexity and fewer model parameters.

本文提出了Hierarchical Multi-person Ordinal Relations (HMOR) 的方法来解决 monocular RGB 相机下 3D multi-person 姿态估计问题，通过构建一个多层的ordinal relation 层级结构从而提高了模型的全局连贯性，进而设计了一套集成的 top-down 模型，在 coarse-to-fine 的框架下同时估计bounding boxes, depths和 root-relative 3D poses，以此达到更加精确的深度估计。与之前的SOTA方法相比，本文的方法在公开的多人3D姿势数据集上取得了显著的优势，且计算复杂度更低、模型参数更少。

HMOR: 分层多人标度关系用于单目多人3D姿势估计