Human Modelling and Pose Estimation Overview

TL;DR

This paper provides a comprehensive overview of human modelling and pose estimation, comparing state-of-the-art algorithms, discussing sensor technologies, applications, challenges, and future research directions in 2D and 3D domains.

Contribution

It offers an up-to-date comparison of SOTA human pose estimation algorithms and insights into current challenges and future prospects in the field.

Findings

Comparison of SOTA 2D and 3D pose estimation algorithms

Discussion of sensor technologies and application areas

Analysis of challenges and future research directions

Abstract

Human modelling and pose estimation stands at the crossroads of Computer Vision, Computer Graphics, and Machine Learning. This paper presents a thorough investigation of this interdisciplinary field, examining various algorithms, methodologies, and practical applications. It explores the diverse range of sensor technologies relevant to this domain and delves into a wide array of application areas. Additionally, we discuss the challenges and advancements in 2D and 3D human modelling methodologies, along with popular datasets, metrics, and future research directions. The main contribution of this paper lies in its up-to-date comparison of state-of-the-art (SOTA) human pose estimation algorithms in both 2D and 3D domains. By providing this comprehensive overview, the paper aims to enhance understanding of 3D human modelling and pose estimation, offering insights into current SOTA achievements, challenges, and future prospects within the field.