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Pose Machines: Articulated Pose Estimation via Inference Machines

  • Varun Ramakrishna
  • Daniel Munoz
  • Martial Hebert
  • James Andrew Bagnell
  • Yaser Sheikh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8690)

Abstract

State-of-the-art approaches for articulated human pose estimation are rooted in parts-based graphical models. These models are often restricted to tree-structured representations and simple parametric potentials in order to enable tractable inference. However, these simple dependencies fail to capture all the interactions between body parts. While models with more complex interactions can be defined, learning the parameters of these models remains challenging with intractable or approximate inference. In this paper, instead of performing inference on a learned graphical model, we build upon the inference machine framework and present a method for articulated human pose estimation. Our approach incorporates rich spatial interactions among multiple parts and information across parts of different scales. Additionally, the modular framework of our approach enables both ease of implementation without specialized optimization solvers, and efficient inference. We analyze our approach on two challenging datasets with large pose variation and outperform the state-of-the-art on these benchmarks.

Keywords

Random Forest Context Feature Composite Part Approximate Inference Pictorial Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Varun Ramakrishna
    • 1
  • Daniel Munoz
    • 1
  • Martial Hebert
    • 1
  • James Andrew Bagnell
    • 1
  • Yaser Sheikh
    • 1
  1. 1.The Robotics InstituteCarnegie Mellon UniversityUSA

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