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.

本综述论文旨在提供关于自中心姿势估计研究的全面概述，包括流行数据集、不同姿势估计模型的分类和讨论，通过对比分析突出不同方法的优缺点。此综述能为研究人员和实践者提供宝贵资源，洞察自中心姿势估计的关键概念、前沿解决方案，以及未来研究领域的待解问题。