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Review of models for estimating 3D human pose using deep learning.

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This review covers advances in 3D human pose estimation (HPE) using deep learning. It highlights challenges in accuracy and real-time performance, offering a roadmap for future research in this rapidly growing computer vision field.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Human Pose Estimation (HPE) detects and localizes human body parts in images/videos.
  • Three-dimensional (3D) HPE determines joint positions in 3D space, a rapidly growing field.
  • HPE has diverse applications in healthcare, security, and entertainment.

Purpose of the Study:

  • To review the latest 3D deep-learning-based HPE models.
  • To address key challenges including accuracy, real-time performance, and data constraints.
  • To provide a roadmap for future research and development in 3D HPE.

Main Methods:

  • Assessment of widely used datasets and evaluation metrics.
  • Comparison of leading 3D HPE algorithms based on precision and computational efficiency.
  • Analysis of deep learning model advancements in 3D human pose estimation.

Main Results:

  • Deep learning models show significant progress in 3D HPE.
  • Persistent challenges include handling occlusion, achieving real-time estimation, and ensuring generalization.
  • Leading algorithms are compared for precision and efficiency.

Conclusions:

  • Deep learning has advanced 3D HPE, but challenges remain.
  • Further research is needed to overcome limitations in occlusion, real-time processing, and model generalization.
  • The review provides a roadmap for future 3D HPE research.