Human Pose Co-Estimation and Applications

M. Eichner, V. Ferrari

Research output: Contribution to journalArticlepeer-review


Most existing techniques for articulated Human Pose Estimation (HPE) consider each person independently. Here we tackle the problem in a new setting, coined Human Pose Coestimation (PCE), where multiple people are in a common, but unknown pose. The task of PCE is to estimate their poses jointly and to produce prototypes characterizing the shared pose. Since the poses of the individual people should be similar to the prototype, PCE has less freedom compared to estimating each pose independently, which simplifies the problem. We demonstrate our PCE technique on two applications. The first is estimating the pose of people performing the same activity synchronously, such as during aerobics, cheerleading, and dancing in a group. We show that PCE improves pose estimation accuracy over estimating each person independently. The second application is learning prototype poses characterizing a pose class directly from an image search engine queried by the class name (e.g., “lotus pose”). We show that PCE leads to better pose estimation in such images, and it learns meaningful prototypes which can be used as priors for pose estimation in novel images.
Original languageEnglish
Pages (from-to)2282-2288
Number of pages7
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number11
Publication statusPublished - 1 Nov 2012


  • image retrieval
  • pose estimation
  • search engines
  • HPE
  • PCE technique
  • aerobics
  • articulated human pose estimation
  • cheerleading
  • dancing
  • human pose coestimation
  • image search engine
  • prototype poses
  • Computational modeling
  • Detectors
  • Estimation
  • Humans
  • Kinematics
  • Prototypes
  • Synchronization
  • Human pose estimation
  • articulated objects
  • multiple image correspondence
  • object detection
  • Algorithms
  • Artificial Intelligence
  • Image Enhancement
  • Image Interpretation, Computer-Assisted
  • Pattern Recognition, Automated
  • Posture
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Subtraction Technique
  • Whole Body Imaging


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