Body Parts Synthesis for Cross-Quality Pose Estimation

Baoyao Yang, Andy J. Ma, Pong C. Yuen*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

6 Citations (Scopus)


Although encouraging results have been obtained in human pose estimation in recent years, the performance may degrade dramatically when the image quality differs between training and testing data sets. This paper addresses problems in cross-image-quality human pose estimation. To achieve this, we follow an unsupervised domain adaptation approach, in which labels in the target domain are unavailable. Unlike existing unsupervised domain adaptation methods that find label information from unlabeled data, the target pose information (label) is instead generated by synthesizing body parts with similar image-quality of the target domain. A translative dictionary is learned to associate the source and target domains, and a cross-quality adaptation model is developed to refine the source pose estimator using the synthesized target body parts. We perform cross-quality experiments on three data sets with different image quality by using two state-of-the-art pose estimators, and compare the proposed method with five unsupervised domain adaptation methods. Our experimental results show that the proposed method outperforms not only the source pose estimators, but also other unsupervised domain adaptation methods.

Original languageEnglish
Article number8245864
Pages (from-to)461-474
Number of pages14
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number2
Early online date3 Jan 2018
Publication statusPublished - Feb 2019

Scopus Subject Areas

  • Media Technology
  • Electrical and Electronic Engineering

User-Defined Keywords

  • domain adaptation
  • Pose estimation


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