Single-Stage Is Enough: Multi-Person Absolute 3D Pose Estimation

Lei Jin, Chenyang Xu, Xiaojuan Wang, Yabo Xiao, Yandong Guo, Xuecheng Nie, Jian Zhao; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 13086-13095

The existing multi-person absolute 3D pose estimation methods are mainly based on two-stage paradigm, i.e., top-down or bottom-up, leading to redundant pipelines with high computation cost. We argue that it is more desirable to simplify such two-stage paradigm to a single-stage one to promote both efficiency and performance. To this end, we present an efficient single-stage solution, Decoupled Regression Model (DRM), with three distinct novelties. First, DRM introduces a new decoupled representation for 3D pose, which expresses the 2D pose in image plane and depth information of each 3D human instance via 2D center point (center of visible keypoints) and root point (denoted as pelvis), respectively. Second, to learn better feature representation for the human depth regression, DRM introduces a 2D Pose-guided Depth Query Module (PDQM) to extract the features in 2D pose regression branch, enabling the depth regression branch to perceive the scale information of instances. Third, DRM leverages a Decoupled Absolute Pose Loss (DAPL) to facilitate the absolute root depth and root-relative depth estimation, thus improving the accuracy of absolute 3D pose. Comprehensive experiments on challenging benchmarks including MuPoTS-3D and Panoptic clearly verify the superiority of our framework, which outperforms the state-of-the-art bottom-up absolute 3D pose estimation methods.