  Recovering multi-person 3D poses with absolute scales from a single RGB image
is a challenging problem due to the inherent depth and scale ambiguity from a
single view. Addressing this ambiguity requires to aggregate various cues over
the entire image, such as body sizes, scene layouts, and inter-person
relationships. However, most previous methods adopt a top-down scheme that
first performs 2D pose detection and then regresses the 3D pose and scale for
each detected person individually, ignoring global contextual cues. In this
paper, we propose a novel system that first regresses a set of 2.5D
representations of body parts and then reconstructs the 3D absolute poses based
on these 2.5D representations with a depth-aware part association algorithm.
Such a single-shot bottom-up scheme allows the system to better learn and
reason about the inter-person depth relationship, improving both 3D and 2D pose
estimation. The experiments demonstrate that the proposed approach achieves the
state-of-the-art performance on the CMU Panoptic and MuPoTS-3D datasets and is
applicable to in-the-wild videos.
