  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.
