3D Human pose estimation: A review of the literature and analysis of covariates

Nikolaos Sarafianos, Bogdan Boteanu, Bogdan Ionescu, Ioannis A. Kakadiaris

Research output: Contribution to journalArticlepeer-review

219 Scopus citations

Abstract

Estimating the pose of a human in 3D given an image or a video has recently received significant attention from the scientific community. The main reasons for this trend are the ever increasing new range of applications (e.g., human-robot interaction, gaming, sports performance analysis) which are driven by current technological advances. Although recent approaches have dealt with several challenges and have reported remarkable results, 3D pose estimation remains a largely unsolved problem because real-life applications impose several challenges which are not fully addressed by existing methods. For example, estimating the 3D pose of multiple people in an outdoor environment remains a largely unsolved problem. In this paper, we review the recent advances in 3D human pose estimation from RGB images or image sequences. We propose a taxonomy of the approaches based on the input (e.g., single image or video, monocular or multi-view) and in each case we categorize the methods according to their key characteristics. To provide an overview of the current capabilities, we conducted an extensive experimental evaluation of state-of-the-art approaches in a synthetic dataset created specifically for this task, which along with its ground truth is made publicly available for research purposes. Finally, we provide an in-depth discussion of the insights obtained from reviewing the literature and the results of our experiments. Future directions and challenges are identified.

Original languageEnglish (US)
Pages (from-to)1-20
Number of pages20
JournalComputer Vision and Image Understanding
Volume152
DOIs
StatePublished - Nov 1 2016

Keywords

  • 3D Human pose estimation
  • Anthropometry
  • Articulated tracking
  • Human motion analysis

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition

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