# Multi-Stage HRNet: Multiple Stage High-Resolution Network for Human Pose Estimation Source: [Multi-Stage HRNet: Multiple Stage High-Resolution Network for Human Pose Estimation]() Collection index: [llms.txt]() Data updated: 2025-10-27T05:59:47.071Z Authors: Junjie Huang, Zheng Zhu, Guan Huang Published: 2019-10-14T03:08:03.000Z ## Abstract Human pose estimation are of importance for visual understanding tasks such as action recognition and human-computer interaction. In this work, we present a Multiple Stage High-Resolution Network (Multi-Stage HRNet) to tackling the problem of multi-person pose estimation in images. Specifically, we follow the top-down pipelines and high-resolution representations are maintained during single-person pose estimation. In addition, multiple stage network and cross stage feature aggregation are adopted to further refine the keypoint position. The resulting approach achieves promising results in COCO datasets. Our single-model-single-scale test configuration obtains 77.1 AP score in test-dev using publicly available training data. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()