Multi-person 3D pose estimation from 3D cloud data using 3D convolutional neural networks

Abstract

Human pose estimation is considered one of the major challenges in the field of Computer Vision, playing an integral role in a large variety of technology domains. While, in the last few years, there has been an increased number of research approaches towards CNN-based 2D human pose estimation from RGB images, respective work on CNN-based 3D human pose estimation from depth/3D data has been rather limited, with current approaches failing to outperform earlier methods, partially due to the utilization of depth maps as simple 2D single-channel images, instead of an actual 3D world representation. In order to overcome this limitation, and taking into consideration recent advances in 3D detection tasks of similar nature, we propose a novel fully-convolutional, detection-based 3D-CNN architecture for 3D human pose estimation from 3D data. The architecture follows the sequential network architecture paradigm, generating per-voxel likelihood maps for each human joint, from a 3D voxel-grid input, and is extended, through a bottom-up approach, towards multi-person 3D pose estimation, allowing the algorithm to simultaneously estimate multiple human poses, without its runtime complexity being affected by the number of people within the scene. The proposed multi-person architecture, which is the first within the scope of 3D human pose estimation, is comparatively evaluated on three single person public datasets, achieving state-of-the-art performance, as well as on a public multi-person dataset achieving high recognition accuracy.