DRP-Net: A Coarse-to-Fine Dynamic Resolution Network for Efficient Real-Time Multi-Person Pose Estimation

Abstract

While real-time multi-person pose estimation is a critical technology for human-computer interaction and action recognition tasks, maintaining accuracy and efficiency on confined hardware remains a major challenge. To overcome the inherent trade-off between the high computational cost of heatmap-based methods and the inferior quality of regression-based ones, this paper uses a coarse-to-fine deep learning mechanism to propose a novel two-stage model named Dynamic Resolution Pose Network (DRP-Net). The model employs a light regression head first for rapid coarse coordinate estimation, then a dynamic refinement head to produce localized heatmaps in small, dense regions of interest to enable precise correction. This effectively maximizes the utilization of computation resources and provides high localization accuracy with significantly reduced model inference latency. Experimental results verify that the medium-sized DRP-Net-M model achieves an Average Precision (AP) of 74.1% on the MS COCO test set at a computation cost of mere 2.15 GFLOPs, outperforming the best-performing real-time model RTMPose-m with a comparable computational budget. This paper presents a two-stage architecture integrating regression and region-localized heatmap refinement. It provides a new high-efficiency paradigm for light-weight real-time pose estimation and sets a new direction to build other dense prediction tasks in computer vision through its dynamic resolution concept.