# Multi-Stage HRNet: Multiple Stage High-Resolution Network for Human Pose Estimation

arXiv: https://arxiv.org/abs/1910.05901 · HTML: https://mlsanity.com/paper/1910.05901/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [RoboGrasp: A Universal Grasping Policy for Robust Robotic Control](https://mlsanity.com/paper/2502.03072/) (2025)
- [Modeling the Uncertainty with Maximum Discrepant Students for Semi-supervised 2D Pose Estimation](https://mlsanity.com/paper/2311.01770/) (2023)

## Abstract (arXiv)

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.