A saliency-based method of simulating visual attention in virtual scenes
Proceedings of the 16th ACM Symposium on Virtual Reality Software and Technology
Virtual gazing in video surveillance
Proceedings of the 2010 ACM workshop on Surreal media and virtual cloning
Eyecatch: simulating visuomotor coordination for object interception
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Virtual humans: back to the future
Proceedings of Graphics Interface 2012
Technical Section: Stream-based animation of real-time crowd scenes
Computers and Graphics
Short paper: exploring the object relevance of a gaze animation model
EGVE - JVRC'11 Proceedings of the 17th Eurographics conference on Virtual Environments & Third Joint Virtual Reality
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Crowd animation is a topic of high interest which offers many challenges. One of the most important is the trade-off between rich, realistic behaviors, and computational costs. To this end, much effort has been put into creating variety in character representation and animation. Nevertheless, one aspect still lacking realism in virtual crowd characters resides in their attention behaviors. In this paper, we propose a framework to add gaze attention behaviors to crowd animations. First, we automatically extract interest points from character or object trajectories in pre- existing animations. For a given character, we assign a set of elementary scores based on parameters such as distance or speed to all other characters or objects in the scene. We then combine these subscores in an overall scoring function. The scores obtained from this function form a set of gaze constraints that determine where and when each character should look. We finally enforce these constraints with an optimized dedicated gaze Inverse Kinematics (IK) solver. It first computes the displacement maps for the constraints to be satisfied. It then smoothly propagates these displacements over an automatically defined number of frames. We demonstrate the efficiency of our method and our visually convincing results through various examples. Copyright © 2009 John Wiley & Sons, Ltd. In this paper, we propose a framework to add gaze attention behaviors to crowd characters. It first consists in automatically detecting the points of interest which should be looked at by the characters. It then consists in a dedicated Inverse Kinematics solver to adapt the character motions in order for them to perform the gaze motions in a smooth and natural way.