  In this paper, we propose a new single shot method for multi-person 3D human
pose estimation in complex images. The model jointly learns to locate the human
joints in the image, to estimate their 3D coordinates and to group these
predictions into full human skeletons. The proposed method deals with a
variable number of people and does not need bounding boxes to estimate the 3D
poses. It leverages and extends the Stacked Hourglass Network and its
multi-scale feature learning to manage multi-person situations. Thus, we
exploit a robust 3D human pose formulation to fully describe several 3D human
poses even in case of strong occlusions or crops. Then, joint grouping and
human pose estimation for an arbitrary number of people are performed using the
associative embedding method. Our approach significantly outperforms the state
of the art on the challenging CMU Panoptic and a previous single shot method on
the MuPoTS-3D dataset. Furthermore, it leads to good results on the complex and
synthetic images from the newly proposed JTA Dataset.
