  Recently, several deep learning models have been proposed for 3D human pose
estimation. Nevertheless, most of these approaches only focus on the
single-person case or estimate 3D pose of a few people at high resolution.
Furthermore, many applications such as autonomous driving or crowd analysis
require pose estimation of a large number of people possibly at low-resolution.
In this work, we present PandaNet (Pose estimAtioN and Dectection Anchor-based
Network), a new single-shot, anchor-based and multi-person 3D pose estimation
approach. The proposed model performs bounding box detection and, for each
detected person, 2D and 3D pose regression into a single forward pass. It does
not need any post-processing to regroup joints since the network predicts a
full 3D pose for each bounding box and allows the pose estimation of a possibly
large number of people at low resolution. To manage people overlapping, we
introduce a Pose-Aware Anchor Selection strategy. Moreover, as imbalance exists
between different people sizes in the image, and joints coordinates have
different uncertainties depending on these sizes, we propose a method to
automatically optimize weights associated to different people scales and joints
for efficient training. PandaNet surpasses previous single-shot methods on
several challenging datasets: a multi-person urban virtual but very realistic
dataset (JTA Dataset), and two real world 3D multi-person datasets (CMU
Panoptic and MuPoTS-3D).
