A Prior Representation-Guided Method for Low-Resolution Human Pose Estimation
Mengting Jiang, Xiaoqi An, Yang Gao, Yalong Xu, Di Wang, Lin Zhao · 2025
Human pose estimation has achieved significant progress on high-resolution (HR) images, but it experiences severe performance degradation on low-resolution (LR) images. One key reason is that LR images lack sufficient appearance details and fine-grained spatial information. In this paper, we propose a prior representation-guided method (PRG) for low-resolution human pose estimation. Our approach consists of two stages: in the first stage, we design a prior representation extraction network to obtain prior representation from HR images. Then we propose dynamic residual blocks that utilize the extracted prior representation to guide the pose estimation network in focusing on detailed features around joint areas. In the second stage, we use a compact diffusion model with fewer iterations to generate the consistent prior representation from LR images, eliminating the reliance on HR images. Extensive experiments demonstrate that our method achieves significant improvements across various resolutions and backbone networks. In particular, our method improves 16.4 AP compared to the SimCC-Res50 baseline at a resolution of 32×32.