Deep High-Resolution Representation Learning for Human Pose Estimation
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Cited by 27 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
This is an official pytorch implementation of Deep High-Resolution Representation Learning for Human Pose Estimation. In this work, 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. The code and models have been publicly available at urlhttps://github.com/leoxiaobin/deep-high-resolution-net.pytorch.
Key facts. Cited by 1,339 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Feb 2019.
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Citing papers per year: 2021: 260; 2022: 271; 2023: 281; 2024: 242; 2025: 186; 2026: 99.
1,339 papers.
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50 most cited of 1,339 All 1,339 (JSON)
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- 2021Conditional DETR for Fast Training Convergencecited by 422
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12 most cited of 1,285 All 1,285 (JSON)
- 2026Detecting Phone-Induced Pedestrian Distraction via a Multimodal Fusion Transformer20 Sep 2026
- 2026Prosthesis-Aware 3D Human Pose Estimation: A Dataset and Benchmark for RSP Users16 Sep 2026
- 2026SignMimic: Robust High-Quality Sign Language Motion Generation via Human-Shape-Oblivious Pose Transfer Guidance12 Sep 2026
- 2026SAFER-Activities: A Dataset for Smart Assessment of Fall Events and Routine Activities7 Sep 2026
- 2026A Black-Box Adversarial Attack on Human Pose Estimation and Keypoint-Based Action Recognition Models7 Sep 2026
- 2026MuyBridge: Mobile Human Center-of-Mass Estimation from Monocular Video via Sparse Fusion2 Sep 2026
- 2026MEOM: Multi-View Expected-OKS Maximization for Human Pose Triangulation31 Aug 2026
- 2026FISICA: A Deployed Service for Plantar-Pressure and Posture Assessment with Ontology-Grounded Recommendation29 Aug 2026
- 2026A comprehensive and trustworthy benchmark of AI methods for change detection in Earth observation28 Aug 2026
- 2026UniFLM: United Segmentation and Measurement on Fetal Limb Ultrasonic Image27 Aug 2026
- 2026Skeleton-based Zero-Shot Spatio-Temporal Action Localization via Weakly-Supervised Pretraining26 Aug 2026
- 2026Markerless Pose Estimation for Resistance Training Technique Assessment25 Aug 2026
12 newest of 1,285 All 1,285 (JSON)