  In this paper, a real-time method called PoP-Net is proposed to predict
multi-person 3D poses from a depth image. PoP-Net learns to predict bottom-up
part detection maps and top-down global poses in a single-shot framework. A
simple and effective fusion process is applied to fuse the global poses and
part detection. Specifically, a new part-level representation, called Truncated
Part Displacement Field (TPDF), is introduced. It drags low-precision global
poses towards more accurate part locations while maintaining the advantage of
global poses in handling severe occlusion and truncation cases. A mode
selection scheme is developed to automatically resolve the conflict between
global poses and local detection. Finally, due to the lack of high-quality
depth datasets for developing and evaluating multi-person 3D pose estimation
methods, a comprehensive depth dataset with 3D pose labels is released. The
dataset is designed to enable effective multi-person and background data
augmentation such that the developed models are more generalizable towards
uncontrolled real-world multi-person scenarios. We show that PoP-Net has
significant advantages in efficiency for multi-person processing and achieves
the state-of-the-art results both on the released challenging dataset and on
the widely used ITOP dataset.
