HMOR: Hierarchical Multi-Person Ordinal Relations for Monocular Multi-Person 3D Pose Estimation

Can Wang, Jiefeng Li, Wentao Liu, Chen Qian, Cewu Lu ;

Abstract


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 (9.2 mm improvement on 3DPW dataset, 12.3 PCK improvement on MuPoTS-3D dataset, and 20.5 mm improvement on CMU Panoptic dataset). In addition to superior performance, our method costs lower computation complexity and fewer model parameters. Our code will be made publicly available.

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