  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.
