FAAST: The Flexible Action and Articulated Skeleton Toolkit

Evan Suma, Belinda Lange, Albert Rizzo, David Krum, Mark Bolas

    Research output: Contribution to conferencePaper

    138 Citations (Scopus)

    Abstract

    The Flexible Action and Articulated Skeleton Toolkit (FAAST) is middleware to facilitate integration of full-body control with virtual reality applications and video games using OpenNI-compliant depth sensors (currently the PrimeSensor and the Microsoft Kinect). FAAST incorporates a VRPN server for streaming the user's skeleton joints over a network, which provides a convenient interface for custom virtual reality applications and games. This body pose information can be used for goals such as realistically puppeting a virtual avatar or controlling an on-screen mouse cursor. Additionally, the toolkit also provides a configurable input emulator that detects human actions and binds them to virtual mouse and keyboard commands, which are sent to the actively selected window. Thus, FAAST can enable natural interaction for existing off-the-shelf video games that were not explicitly developed to support input from motion sensors. The actions and input bindings are configurable at run-time, allowing the user to customize the controls and sensitivity to adjust for individual body types and preferences. In the future, we plan to substantially expand FAAST's action lexicon, provide support for recording and training custom gestures, and incorporate real-time head tracking using computer vision techniques.

    Original languageEnglish
    Pages247-248
    Number of pages2
    DOIs
    Publication statusPublished - 25 May 2011
    Event18th IEEE Virtual Reality Conference -
    Duration: 19 Mar 2011 → …

    Conference

    Conference18th IEEE Virtual Reality Conference
    Period19/03/11 → …

    Keywords

    • depth-sensing cameras
    • gestures
    • middleware
    • video games

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  • Cite this

    Suma, E., Lange, B., Rizzo, A., Krum, D., & Bolas, M. (2011). FAAST: The Flexible Action and Articulated Skeleton Toolkit. 247-248. Paper presented at 18th IEEE Virtual Reality Conference, . https://doi.org/10.1109/VR.2011.5759491