A Survey on 3D Egocentric Human Pose Estimation
It addresses the need for a comprehensive resource for researchers and practitioners in fields like XR-technologies and human-computer interaction, but it is incremental as it synthesizes existing work rather than introducing new methods.
This survey paper tackles the lack of a systematic literature review on 3D egocentric human pose estimation by providing an extensive overview of current research, including datasets, models, and comparative analysis of methods.
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.