A Survey on 3D Egocentric Human Pose Estimation

TL;DR

This survey reviews recent advances in egocentric 3D human pose estimation, categorizing datasets and models, and analyzing their strengths and weaknesses to guide future research in this rapidly evolving field.

Contribution

It provides the first comprehensive literature review on egocentric 3D human pose estimation, summarizing datasets, models, and identifying open challenges.

Findings

Comparison of datasets and their suitability for egocentric pose estimation

Analysis of different pose estimation models and their performance

Identification of open problems and future research directions

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.