Capturing Hands in Action using Discriminative Salient Points and Physics Simulation




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.

Author(s): Dimitrios Tzionas and Luca Ballan and Abhilash Srikantha and Pablo Aponte and Marc Pollefeys and Juergen Gall
Journal: International Journal of Computer Vision (IJCV)
Year: 2016

Department(s): Perceiving Systems
Research Project(s): Hands in Action
Bibtex Type: Article (article)
Paper Type: Journal

DOI: 10.1007/s11263-016-0895-4
State: Published

Links: Website


  title = {Capturing Hands in Action using Discriminative Salient Points and Physics Simulation},
  author = {Tzionas, Dimitrios and Ballan, Luca and Srikantha, Abhilash and Aponte, Pablo and Pollefeys, Marc and Gall, Juergen},
  journal = {International Journal of Computer Vision (IJCV)},
  year = {2016}