Monocular human pose estimation: A survey of deep learning-based methods

By Yucheng Chen, Yingli Tian, Mingyi He. Computer Vision and Image Understanding, 2020

412 citations recorded by the source corpus.

Abstract Vision-based monocular human pose estimation, as one of the most fundamental and challenging problems in computer vision, aims to obtain posture of the human body from input images or video sequences. The recent developments of deep learning techniques have been brought significant progress and remarkable breakthroughs in the field of human pose estimation. This survey extensively reviews the recent deep learning-based 2D and 3D human pose estimation methods published since 2014. This paper summarizes the challenges, main frameworks, benchmark datasets, evaluation metrics, performance comparison, and discusses some promising future research directions.

Subfields: Computer Vision and Pattern Recognition, Endocrinology, Diabetes and Metabolism

View Paper: https://www.semanticscholar.org/paper/81c2145a73ab64a7bec695616d34486a860d9051

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Chen Chen
2. Nasser Kehtarnavaz
3. Taojiannan Yang
4. Sijie Zhu
5. Ce Zheng
6. Ju Shen
7. Ruixu Liu
8. Wenhan Wu

In Endocrinology, Diabetes and Metabolism

1. Nasser Kehtarnavaz

Citations in the corpus

• Deep Learning-based Human Pose Estimation: A Survey (2020, methodology): “The kinematic model, also called skeleton-based model [12] or kinematic chain model [14], as shown in Fig.”
• Deep 3D human pose estimation: A review (2021, neutral): “For example, Chen et al. (2020) merely provide a review of deep learning-based methods for monocular human pose estimation.”