Abstract
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.
Abstract (translated)
自我中心化的人类姿态估计旨在估计人类身体姿态并从第一人称视角开发人体表示。近年来,由于其在XR技术、人机交互和健身跟踪等领域的广泛应用,该技术已经取得了巨大的人气。然而,据我们所知,目前尚无基于所提出的解决方案的系统性文献综述。因此,本文的调查论文旨在提供关于自中心化3D人类姿态估计研究现状的全面概述。在本文中,我们将对流行的数据集进行分类和讨论,并讨论不同姿态估计模型的优缺点,通过比较分析突出不同方法的优缺点。本调查可以成为该领域研究人员和实践者的宝贵资源,为对自中心化姿态估计、其广泛应用以及未来具有开放性的问题提供深入了解。
URL
https://arxiv.org/abs/2403.17893
