Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation

TL;DR

The paper introduces Keypoints as Dynamic Centroid (KDC), a novel centroid-based method that unifies human pose estimation and segmentation, improving accuracy and speed in complex, dynamic scenarios.

Contribution

KDC is a new centroid-based approach that enhances joint detection and segmentation by using high-confidence keypoints as dynamic centroids for clustering.

Findings

Outperforms existing methods on CrowdPose, OCHuman, and COCO benchmarks.

Effective in challenging scenarios with overlapping joints and rapid movements.

Achieves better accuracy and runtime performance.

Abstract

The dynamic movement of the human body presents a fundamental challenge for human pose estimation and body segmentation. State-of-the-art approaches primarily rely on combining keypoint heatmaps with segmentation masks but often struggle in scenarios involving overlapping joints or rapidly changing poses during instance-level segmentation. To address these limitations, we propose Keypoints as Dynamic Centroid (KDC), a new centroid-based representation for unified human pose estimation and instance-level segmentation. KDC adopts a bottom-up paradigm to generate keypoint heatmaps for both easily distinguishable and complex keypoints and improves keypoint detection and confidence scores by introducing KeyCentroids using a keypoint disk. It leverages high-confidence keypoints as dynamic centroids in the embedding space to generate MaskCentroids, allowing for swift clustering of pixels to specific human instances during rapid body movements in live environments. Our experimental evaluations on the CrowdPose, OCHuman, and COCO benchmarks demonstrate KDC's effectiveness and generalizability in challenging scenarios in terms of both accuracy and runtime performance. The implementation is available at: https://sites.google.com/view/niazahmad/projects/kdc.