A Survey on 3D Egocentric Human Pose Estimation

Md Mushfiqur Azam, Kevin Desai; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 1643-1654

Egocentric human pose estimation aims to estimate human body poses and develop body representations from a first-person camera perspective. It has gained vast popularity in recent years because of its wide range of applications in sectors like XR-technologies human-computer interaction and fitness tracking. However to the best of our knowledge there is no systematic literature review based on the proposed solutions regarding egocentric 3D human pose estimation. To that end the aim of this survey paper is to provide an extensive overview of the current state of egocentric pose estimation research. In this paper we categorize and discuss the popular datasets and the different pose estimation models highlighting the strengths and weaknesses of different methods by comparative analysis. This survey can be a valuable resource for both researchers and practitioners in the field offering insights into key concepts and cutting-edge solutions in egocentric pose estimation its wide-ranging applications as well as the open problems with future scope.