International Journal of Computer Vision

, Volume 118, Issue 2, pp 172–193 | Cite as

Capturing Hands in Action Using Discriminative Salient Points and Physics Simulation

  • Dimitrios Tzionas
  • Luca Ballan
  • Abhilash Srikantha
  • Pablo Aponte
  • Marc Pollefeys
  • Juergen Gall
Article

Abstract

Hand motion capture is a popular research field, recently gaining more attention due to the ubiquity of RGB-D sensors. However, even most recent approaches focus on the case of a single isolated hand. In this work, we focus on hands that interact with other hands or objects and present a framework that successfully captures motion in such interaction scenarios for both rigid and articulated objects. Our framework combines a generative model with discriminatively trained salient points to achieve a low tracking error and with collision detection and physics simulation to achieve physically plausible estimates even in case of occlusions and missing visual data. Since all components are unified in a single objective function which is almost everywhere differentiable, it can be optimized with standard optimization techniques. Our approach works for monocular RGB-D sequences as well as setups with multiple synchronized RGB cameras. For a qualitative and quantitative evaluation, we captured 29 sequences with a large variety of interactions and up to 150 degrees of freedom.

Keywords

Hand motion capture Hand–object interaction Fingertip detection Physics simulation 

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Dimitrios Tzionas
    • 1
    • 2
  • Luca Ballan
    • 3
  • Abhilash Srikantha
    • 1
    • 2
  • Pablo Aponte
    • 1
  • Marc Pollefeys
    • 3
  • Juergen Gall
    • 1
  1. 1.Institute of Computer Science IIIUniversity of BonnBonnGermany
  2. 2.Perceiving Systems DepartmentMax Planck institute for Intelligent SystemsTübingenGermany
  3. 3.Institute for Visual Computing, ETH ZurichUniversitätstraße 6ZurichSwitzerland

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