mlsanityData to 7 Oct 2026

Human Modelling and Pose Estimation Overview

arXiv 2406.19290 ↗First posted 27 Jun 2024cs.CVcited by 4

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Abstract (arXiv)

Human modelling and pose estimation stands at the crossroads of Computer Vision, Computer Graphics, and Machine Learning. This paper presents a thorough investigation of this interdisciplinary field, examining various algorithms, methodologies, and practical applications. It explores the diverse range of sensor technologies relevant to this domain and delves into a wide array of application areas. Additionally, we discuss the challenges and advancements in 2D and 3D human modelling methodologies, along with popular datasets, metrics, and future research directions. The main contribution of this paper lies in its up-to-date comparison of state-of-the-art (SOTA) human pose estimation algorithms in both 2D and 3D domains. By providing this comprehensive overview, the paper aims to enhance understanding of 3D human modelling and pose estimation, offering insights into current SOTA achievements, challenges, and future prospects within the field.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 104 of its 157 references that are in the map. First posted on arXiv 27 Jun 2024.

Builds on

104 of 157 references.

References · 104

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38 more
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  21. 2018CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmarkcited by 214
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50 most cited of 104 All 104 (JSON)

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Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025Human Motion Video Generation: A Surveycited by 25
  2. 2025Towards Biomarker Discovery for Early Cerebral Palsy Detection: Evaluating Explanations Through Kinematic Perturbationscited by 1
  3. 2025Improvement of human health lifespan with hybrid group pose estimation methodscited by 0
  4. 20253D Human Pose and Shape Estimation from LiDAR Point Clouds: A Reviewcited by 0