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Human Modelling and Pose Estimation Overview

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arxiv 2406.19290 v1 pith:RYQDCC57 submitted 2024-06-27 cs.CV

Human Modelling and Pose Estimation Overview

classification cs.CV
keywords humanestimationmodellingposealgorithmschallengescomputerfield
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 1 Pith paper

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    A comprehensive survey with a five-phase pipeline model for human motion video generation, covering over 200 papers and adding a new benchmark comparison of nine pose-guided methods.