Advancements in Human Pose Estimation: A Review of Key Studies and Findings Till 2025

Abstract

This paper presents an in-depth literature review that comprehensively covers the major developments, methods, architectures and datasets used in the field of human pose prediction up to 2025. The review covers a broad spectrum, starting with traditional methods, deep learning-based techniques, convolutional neural networks, graph-based approaches and more recently prominent transformer-based models. In addition to two-dimensional (2D) and three-dimensional (3D) human pose estimation methods, the paper analyses in detail the diversity of data sets, applications of Microsoft Kinect technology, real-time pose estimation systems and related architectural designs. Overall, the review of more than 120 papers shows that existing systems have made significant progress in terms of accuracy, computational efficiency and practical applications, but that there are still some challenges to overcome in complex scenarios such as multiple person detection, occlusion problems and outdoor environments. This in-depth analysis highlights current trends in the field, future research directions and potential applications.