A gaze-responsive self-disclosing display
CHI '90 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selective quality rendering by exploiting human inattentional blindness: looking but not seeing
VRST '02 Proceedings of the ACM symposium on Virtual reality software and technology
Eye Tracking Methodology: Theory and Practice
Eye Tracking Methodology: Theory and Practice
Detail to attention: exploiting visual tasks for selective rendering
EGRW '03 Proceedings of the 14th Eurographics workshop on Rendering
Visual attention for efficient high-fidelity graphics
Proceedings of the 21st spring conference on Computer graphics
Proceedings of the 2006 symposium on Eye tracking research & applications
Visual attention in 3D video games
Proceedings of the 2006 ACM SIGCHI international conference on Advances in computer entertainment technology
Perceptual rendering of participating media
ACM Transactions on Applied Perception (TAP)
Applying computational tools to predict gaze direction in interactive visual environments
ACM Transactions on Applied Perception (TAP)
Real-time tracking of visually attended objects in interactive virtual environments
Proceedings of the 2007 ACM symposium on Virtual reality software and technology
Using semantic content as cues for better scanpath prediction
Proceedings of the 2008 symposium on Eye tracking research & applications
A psychophysical study of fixation behavior in a computer game
Proceedings of the 5th symposium on Applied perception in graphics and visualization
Game design guided by visual attention
ICEC'07 Proceedings of the 6th international conference on Entertainment Computing
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SG'11 Proceedings of the 11th international conference on Smart graphics
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Proceedings of the 6th International Conference on Foundations of Digital Games
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Gaze analysis and prediction in interactive virtual environments, such as games, is a challenging topic since the 3D perspective and variations of the viewpoint as well as the current task introduce many variables that affect the distribution of gaze. In this article, we present a novel pipeline to study eye-tracking data acquired from interactive 3D applications. The result of the pipeline is an importance map which scores the amount of gaze spent on each object. This importance map is then used as a heuristic to predict a user's visual attention according to the object properties present at runtime. The novelty of this approach is that the analysis is performed in object space and the importance map is defined in the feature space of high-level properties. High-level properties are used to encode task relevance and other attributes, such as eccentricity, which may have an impact on gaze behavior. The pipeline has been tested with an exemplary study on a first-person shooter game. In particular, a protocol is presented describing the data acquisition procedure, the learning of different importance maps from the data, and finally an evaluation of the performance of the derived gaze predictors. A metric measuring the degree of correlation between attention predicted by the importance map and the actual gaze yielded clearly positive results. The correlation becomes particularly strong when the player is attentive to an in-game task.