Deep High-Resolution Representation Learning for Human Pose Estimation
Abstract
In this paper, we are interested in the human pose estimation problem with a focus on learning reliable high-resolution representations. Most existing methods recover high-resolution representations from low-resolution representations produced by a high-to-low resolution network. Instead, our proposed network maintains high-resolution representations through the whole process. We start from a high-resolution subnetwork as the first stage, gradually add high-to-low resolution subnetworks one by one to form more stages, and connect the mutli-resolution subnetworks in parallel. We conduct repeated multi-scale fusions such that each of the high-to-low resolution representations receives information from other parallel representations over and over, leading to rich high-resolution representations. As a result, the predicted keypoint heatmap is potentially more accurate and spatially more precise. We empirically demonstrate the effectiveness of our network through the superior pose estimation results over two benchmark datasets: the COCO keypoint detection dataset and the MPII Human Pose dataset. In addition, we show the superiority of our network in pose tracking on the PoseTrack dataset. The code and models have been publicly available at https://github.com/leoxiaobin/deep-high-resolution-net.pytorch.
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Both HRNet and Lite-HRNet have been used in spacecraft pose estimation pipelines.
“Both HRNet [25] and Lite-HRNet [69] have been used in spacecraft pose estimation pipelines [16, 40].”
From Domain Generalization for In-Orbit 6D Pose Estimation (2025) · cites this paper as [25]
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Li, K., Wang, S., Zhang, X., Xu, Y., Xu, W., & Tu, Z. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00198 · Cited by 301 · see citing works
FuseSeg: Semantic segmentation of urban scenes based on RGB and thermal data fusion
Sun, Y., Zuo, W., Yun, P., Wang, H., & Liu, M. (2021) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2020.2993143 · Cited by 270 · see citing works
Deep Learning for Generic Object Detection: A Survey
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2018) · arXiv · doi:10.48550/arxiv.1809.02165 · Cited by 2,965 · see citing works
A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives
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Zhang, Q., Zhao, S., Luo, Y., Zhang, D., Huang, N., & Han, J. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00266 · Cited by 225 · see citing works
DetNAS: Backbone Search for Object Detection
Chen, Y., Yang, T., Zhang, X., Meng, G., Xiao, X., & Sun, J. (2019) · arXiv · doi:10.48550/arxiv.1903.10979 · Cited by 205 · see citing works
Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement
Chen, B., Cao, J., Parra, A., & Chin, T.-J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2019.00343 · Cited by 203 · see citing works
BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion
Black, M. J., Patel, P., Tesch, J., & Yang, J. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00843 · Cited by 148 · see citing works
HRFormer: High-Resolution Transformer for Dense Prediction
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Self-citationATRW: A Benchmark for Amur Tiger Re-identification in the Wild
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“6 [107] -NAEPnP refined with a Huber style geometric loss optimised as non-linear least-squares problem 0.”
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“A High-Resolution Net (HRNet) [52] is then trained to regress the location of projected 2D corner point landmarks on the spacecraft from the input greyscale image.”
MethodSatellite Pose Estimation Competition 2021: Results and Analyses
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Learning Privacy-preserving Optics for Human Pose Estimation
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SkeletonMAE: Spatial-Temporal Masked Autoencoders for Self-Supervised Skeleton Action Recognition
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Mesh-based Dynamics with Occlusion Reasoning for Cloth Manipulation
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Towards Bridging the Space Domain Gap for Satellite Pose Estimation using Event Sensing
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Spacecraft Homography Pose Estimation with Single-Stage Deep Convolutional Neural Network
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“The CNN-based high-resolution network (HRNET-w32/48) [26] has shown great performance for terrestrial datasets in the applications of key points extraction and pose estimation.”
Spatial-Aware Regression for Keypoint Localization
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A Monocular Pose Estimation Case Study: The Hayabusa2 Minerva-II2 Deployment
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Domain Generalization for In-Orbit 6D Pose Estimation
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Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models
Jocher, G., Qiu, J., Liu, M., Lyu, S., Akyon, F. C., & Kalfaoglu, M. E. (2026) · arXiv · doi:10.48550/arxiv.2606.03748 · Cited by 16 · see citing works
“HRNet maintains high-resolution representations through parallel subnetwork fusion [64], while OpenPose takes a bottom-up approach by jointly predicting keypoints and Part Affinity Fields [4].”
- Authors
- Sun, K., Xiao, B., Liu, D., & Wang, J.
- Affiliations
- University of Science and Technology of China (CN)
- Microsoft Research Asia (China) (CN)
- Microsoft (United States) (US)
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 5686-5696
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2916798096
- Cited by
- 5,984
- References
- 74
- Citations by year
- 2026: 555 · 2025: 1008 · 2024: 1168 · 2023: 1178 · 2022: 867 · 2021: 737 · 2020: 405 · 2019: 64 · 2018: 1 · 2014: 1
- FWCI
- 168.96
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- human pose estimation, multi-scale feature fusion, keypoint detection, pose tracking
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