FCR-PoseHRNet: Flexible Feature Realignment and Cross-Resolution Coordinate Refinement in PoseHRNet for 2D Human Pose Estimation

Ali Zakir, Gibran Benitez-Garcia, Hiroki Takahashi

Abstract: Human Pose Estimation (HPE) remains challenging due to variations in body scales, frequent occlusions, and high computational requirements. Although heatmap-based methods achieve strong accuracy, they typically rely on large upsampling operations, leading to significant memory usage and complicating deployment in real-time or resource-constrained devices. In this paper, we propose FCR-PoseHRNet, Flexible Feature Realignment, and Cross-Resolution Coordinate Refinement, a coordinate-based framework that addresses these issues. Our approach modifies the HRNet backbone by replacing standard convolutions with depthwise separable variants, creating an efficient architecture that preserves strong representational power. To handle complex poses and occluded keypoints, we introduce Adaptive Feature Realignment (AFR) to globally adjust feature distributions, alongside a Cross-Resolution Channel Attention (CRCA) module for effective multi-scale feature fusion. Finally, a Fractal Coordinate Refinement (FCR) procedure iteratively refines joint coordinates, avoiding the need for large heatmap predictions. Evaluations on the COCO, CrowdPose, and MPII benchmarks show that FCR-PoseHRNet achieves competitive pose accuracy compared to state-of-the-art methods while significantly reducing computational overhead, confirming its suitability for real-time applications under challenging conditions.

External IDs:dblp:conf/acivs/ZakirBT25