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Single Person Pose Estimation: A Survey

arXiv 2109.10056 ↗First posted 21 Sep 2021cs.CVcited by 4

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Abstract (arXiv)

Human pose estimation in unconstrained images and videos is a fundamental computer vision task. To illustrate the evolutionary path in technique, in this survey we summarize representative human pose methods in a structured taxonomy, with a particular focus on deep learning models and single-person image setting. Specifically, we examine and survey all the components of a typical human pose estimation pipeline, including data augmentation, model architecture and backbone, supervision representation, post-processing, standard datasets, evaluation metrics. To envisage the future directions, we finally discuss the key unsolved problems and potential trends for human pose estimation.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 61 of its 95 references that are in the map. First posted on arXiv 21 Sep 2021.

Builds on

61 of 95 references.

References · 61

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38 more
  1. 2014Efficient Object Localization Using Convolutional Networkscited by 226
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  19. 2018PoseFix: Model-agnostic General Human Pose Refinement Networkcited by 47
  20. 2018Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimationcited by 44
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  24. 2017Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimationcited by 35
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50 most cited of 61 All 61 (JSON)

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Cited by year

Citing papers per year: 2023: 2; 2024: 0; 2025: 2.

4 papers.

  1. 2025Towards Robust and Realistic Human Pose Estimation via WiFi Signalscited by 5
  2. 2023Pose2Gait: Extracting Gait Features from Monocular Video of Individuals with Dementiacited by 2
  3. 2025GraphEnet: Event-driven Human Pose Estimation with a Graph Neural Networkcited by 1
  4. 2023Augmenting Vision-Based Human Pose Estimation with Rotation Matrixcited by 0