Abstract
This comprehensive review article explores the latest research advancements in the realm of estimating 3D human pose. Traditional methods such as PSM, SVM are discussed. Besides, this review also talks about deep learning-based approaches, including direct approaches, 2D-to-3D lifting and volumetric model approach for single person, top-down approaches and bottom-up approaches for multi-person pose estimation. The analysis covers the strengths and challenges of various methods, encompassing issues such as model generalization, occlusion robustness, and computational efficiency. Current research issues are identified, and future directions are proposed. By summarizing and evaluating existing methods, this paper aims to provide valuable insights for researchers in both academia and industry, driving the evolution of 3D human pose estimation for better practical applications.
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