PI-Net: Pose Interacting Network for Multi-Person Monocular 3D Pose Estimation

Recent literature addressed the monocular 3D pose estimation task verysatisfactorily. In these studies, different persons are usually treated asindependent pose instances to estimate. However, in many every-day situations,people are interacting, and the pose of an individual depends on the pose ofhis/her interactees. In this paper, we investigate how to exploit thisdependency to enhance current - and possibly future - deep networks for 3Dmonocular pose estimation. Our pose interacting network, or PI-Net, inputs theinitial pose estimates of a variable number of interactees into a recurrentarchitecture used to refine the pose of the person-of-interest. Evaluating sucha method is challenging due to the limited availability of public annotatedmulti-person 3D human pose datasets. We demonstrate the effectiveness of ourmethod in the MuPoTS dataset, setting the new state-of-the-art on it.Qualitative results on other multi-person datasets (for which 3D poseground-truth is not available) showcase the proposed PI-Net. PI-Net isimplemented in PyTorch and the code will be made available upon acceptance ofthe paper.