FAAST: The Flexible Action and Articulated Skeleton Toolkit

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...

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Bibliographic Details
Published in:2011 IEEE Virtual Reality Conference pp. 247 - 248
Main Authors: Suma, E A, Lange, B, Rizzo, A, Krum, D M, Bolas, M
Format: Conference Proceeding
Language:English
Published: IEEE 01.03.2011
Subjects:
depth-sensing cameras
Games
gestures
Joints
Mice
middleware
Sensors
Software
video games
Virtual reality
ISBN:9781457700392, 1457700395
ISSN:1087-8270
Online Access:Get full text
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Summary: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.
ISBN:9781457700392
1457700395
ISSN:1087-8270
DOI:10.1109/VR.2011.5759491