Skip to Main content Skip to Navigation
Journal articles

A review of 3D human pose estimation algorithms for markerless motion capture: review

Abstract : Human pose estimation is a very active research field, stimulated by its important applications in robotics, entertainment or health and sports sciences, among others. Advances in convolutional networks triggered noticeable improvements in 2D pose estimation, leading modern 3D markerless motion capture techniques to an average error per joint of 20 mm. However, with the proliferation of methods, it is becoming increasingly difficult to make an informed choice. Here, we review the leading human pose estimation methods of the past five years, focusing on metrics, benchmarks and method structures. We propose a taxonomy based on accuracy, speed and robustness that we use to classify de methods and derive directions for future research.
Document type :
Journal articles
Complete list of metadata
Contributor : Administrateur IMT - Mines Alès Connect in order to contact the contributor
Submitted on : Tuesday, October 12, 2021 - 2:23:42 PM
Last modification on : Friday, August 5, 2022 - 10:58:25 AM
Long-term archiving on: : Thursday, January 13, 2022 - 7:42:43 PM


Files produced by the author(s)



Yann Desmarais, Denis Mottet, Pierre Slangen, Philippe Montesinos. A review of 3D human pose estimation algorithms for markerless motion capture: review. Computer Vision and Image Understanding, Elsevier, 2021, 212, pp.103275. ⟨10.1016/j.cviu.2021.103275⟩. ⟨hal-03344404⟩



Record views


Files downloads