Three-dimensional human pose estimation has made significant advancements through the
integration of deep learning techniques. This survey provides a comprehensive review of …

Abstract 3D human pose estimation and mesh recovery have attracted widespread research
interest in many areas, such as computer vision, autonomous driving, and robotics. Deep …

Learning-based methods have dominated the 3D human pose estimation (HPE) tasks with
significantly better performance in most benchmarks than traditional optimization-based …

Traditional methods for human localization and pose estimation (HPE), which mainly rely on
RGB images as an input modality, confront substantial limitations in real-world applications …

The goal of 2D human pose estimation (HPE) is to localize anatomical landmarks, given an
image of a person in a pose. SOTA techniques make use of thousands of labeled figures …

The confidence scores of 2D pose estimation are widely utilized in various fields, including
multi-view 3D human pose estimation, skeleton-based human tracking, human action …

Three-dimensional human pose estimation focuses on generating 3D pose sequences from
2D videos. It has enormous potential in the fields of human–robot interaction, remote …

One motivation for studying semi-supervised techniques for human pose estimation is to
compensate for the lack of variety in curated 3D human pose datasets by combining labeled …

This article proposes a horizontal pose fusion framework based on mirror images to address
the data limitations of single-view three-dimensional (3D) pose estimation and the need for …

To make up for the inherent challenging nature of 3D pose estimation, most multi-view
frameworks rely on camera calibration, often leading to impractical or constrained …