  In this paper, we present a novel Distribution-Aware Single-stage (DAS) model
for tackling the challenging multi-person 3D pose estimation problem. Different
from existing top-down and bottom-up methods, the proposed DAS model
simultaneously localizes person positions and their corresponding body joints
in the 3D camera space in a one-pass manner. This leads to a simplified
pipeline with enhanced efficiency. In addition, DAS learns the true
distribution of body joints for the regression of their positions, rather than
making a simple Laplacian or Gaussian assumption as previous works. This
provides valuable priors for model prediction and thus boosts the
regression-based scheme to achieve competitive performance with volumetric-base
ones. Moreover, DAS exploits a recursive update strategy for progressively
approaching to regression target, alleviating the optimization difficulty and
further lifting the regression performance. DAS is implemented with a fully
Convolutional Neural Network and end-to-end learnable. Comprehensive
experiments on benchmarks CMU Panoptic and MuPoTS-3D demonstrate the superior
efficiency of the proposed DAS model, specifically 1.5x speedup over previous
best model, and its stat-of-the-art accuracy for multi-person 3D pose
estimation.
