  Although significant improvement has been achieved recently in 3D human pose
estimation, most of the previous methods only treat a single-person case. In
this work, we firstly propose a fully learning-based, camera distance-aware
top-down approach for 3D multi-person pose estimation from a single RGB image.
The pipeline of the proposed system consists of human detection, absolute 3D
human root localization, and root-relative 3D single-person pose estimation
modules. Our system achieves comparable results with the state-of-the-art 3D
single-person pose estimation models without any groundtruth information and
significantly outperforms previous 3D multi-person pose estimation methods on
publicly available datasets.
  The code is available in https://github.com/mks0601/3DMPPE_ROOTNET_RELEASE ,
https://github.com/mks0601/3DMPPE_POSENET_RELEASE.
