论文标题
HMOR：单眼多人3D姿势估计的分层多人关系
HMOR: Hierarchical Multi-Person Ordinal Relations for Monocular Multi-Person 3D Pose Estimation
论文作者
论文摘要
从单眼RGB摄像机估计3D人姿势估算中，取得了显着进展。但是，只有少数研究探讨了3D多人案例。在本文中，我们试图通过引入一种新颖的监督形式 - 等级多人关系（HMOR）来解决自上而下方法的全球视角。 HMOR编码相互作用信息作为深度和角度层次的序数关系，从而捕获了身体零件和关节水平的语义，并同时保持全局一致性。在我们的方法中，综合自上而下的模型旨在在学习过程中利用这些序数关系。综合模型估计人的边界框，人类深度和根层的3D同时摆放，并具有粗到美的结构，以提高深度估计的准确性。在公开可用的多人3D姿势数据集上，提出的方法大大优于最先进的方法。除了出色的性能外，我们的方法还要花费较低的计算复杂性和更少的模型参数。
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.
