VisualCent: Visual Human Analysis using Dynamic Centroid Representation

Abstract

We introduce VisualCent, a unified human pose and instance segmentation framework to address generalizability and scalability limitations to multi-person visual human analysis. VisualCent leverages centroid-based bottomup keypoint detection paradigm and uses Keypoint Heatmap incorporating Disk Representation and KeyCentroid to identify the optimal keypoint coordinates. For the unified segmentation task, an explicit keypoint is defined as a dynamic centroid called MaskCentroid to swiftly cluster pixels to specific human instance during rapid changes in human body movement or significantly occluded environment. Experimental results on COCO and OCHuman datasets demonstrate VisualCent's accuracy and real-time performance advantages, outperforming existing methods in mAP scores and execution frame rate per second. The implementation is available on the project page††https://sites.google.com/view/niazahmad/projects/visualcent © 2025 Elsevier B.V., All rights reserved.