MMDA: Multi-person marginal distribution awareness for monocular 3D pose estimation

Most existing 3D pose representations cannot completely decouple the overlapping two or more human joints of the same type. In this paper, the authors propose a novel 2.5 D representation of the human pose by projecting human joints in 3D space onto the three orthogonal planes. The authors apply for the first time the permutation module to a multi-person 3D human pose estimation task and use Geometric Constraints Loss (GCL) to guide the learning of the model. The authors overcome the negative effects of the inductive bias of convolutional neural networks (CNNs) by aligning the intermediate feature space with the output feature space. The effectiveness of the authors' approach is validated on the carnegie mellon university (CMU) panoptic dataset and MuPoTS-3D dataset. The authors' proposed representations can effectively decouple the human joints in their selected data from overlapping human joints.