Capturing Hands in Action using Discriminative Salient Points and Physics Simulation

2016

Article

ps

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.

@article{Tzionas:IJCV:2016,
  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)},
  volume = {118},
  number = {2},
  pages = {172--193},
  month = jun,
  year = {2016},
  doi = {10.1007/s11263-016-0895-4},
  url = {https://doi.org/10.1007/s11263-016-0895-4},
  month_numeric = {6}
}