  Human-centric perception (e.g. detection, segmentation, pose estimation, and
attribute analysis) is a long-standing problem for computer vision. This paper
introduces a unified and versatile framework (HQNet) for single-stage
multi-person multi-task human-centric perception (HCP). Our approach centers on
learning a unified human query representation, denoted as Human Query, which
captures intricate instance-level features for individual persons and
disentangles complex multi-person scenarios. Although different HCP tasks have
been well-studied individually, single-stage multi-task learning of HCP tasks
has not been fully exploited in the literature due to the absence of a
comprehensive benchmark dataset. To address this gap, we propose COCO-UniHuman
benchmark to enable model development and comprehensive evaluation.
Experimental results demonstrate the proposed method's state-of-the-art
performance among multi-task HCP models and its competitive performance
compared to task-specific HCP models. Moreover, our experiments underscore
Human Query's adaptability to new HCP tasks, thus demonstrating its robust
generalization capability. Codes and data are available at
https://github.com/lishuhuai527/COCO-UniHuman.
