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Chapter 5. Interaction with Virtual Crowds for VRET of Agoraphobia
Finally, we have used this setup together with a gesture recognition algorithm developed
by van der Pol [van der Pol, 2009] in order to obtain increased interaction between user and
virtual characters. The gesture recognition is done by tracking the user’s hand. We thus use
a data glove with Light-Emitting Diodes (LEDs) which is also part of the Phasespace motion
capture system.
5.1 System Overview
The overall setup of our system is depicted in Figure 5.1. It illustrates the various elements
composing it and how they are interconnected. The various trackers (eye-tracker, headtracker
and data glove) send their position and orientation information to their dedicated
software on different PCs. All this information is then gathered by the PC running the simulation
and used to correctly render the images on the various CAVE screens and define the
character gaze and gesture behaviors.
Figure 5.1: Hardware setup in the CAVE environment.
The pipeline for the gaze behaviors is depicted in Figure 5.2. The Simulation, Animation
and Rendering phases in the diagram all belong to the crowd simulation engine we have
implemented our system in [Maïm et al., 2009]. This diagram shows the real-time simulation
loop pipeline. Other steps occur beforehand, such as the loading of the character templates
and scene. In the real-time simulation loop, our system integrates after the Animation step.
The crowd characters are thus animated with walking gestures for example. The motion
adaptation that we discuss in the Gaze section is therefore applied on top of the current
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