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Gaze prediction using machine learning for dynamic stereo manipulation in games

Koulieris Georgios-Alexandros, Drettakis, George, 1965-, Cunningham Douglas W., Mania Aikaterini

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URIhttp://purl.tuc.gr/dl/dias/06A71739-08BE-46A4-99F7-C41C6C1639B9-
Identifierhttps://ieeexplore.ieee.org/document/7504694/-
Identifierhttps://doi.org/10.1109/VR.2016.7504694-
Languageen-
Extent8 pagesen
TitleGaze prediction using machine learning for dynamic stereo manipulation in gamesen
CreatorKoulieris Georgios-Alexandrosen
CreatorΚουλιερης Γεωργιος-Αλεξανδροςel
CreatorDrettakis, George, 1965-en
CreatorCunningham Douglas W.en
CreatorMania Aikaterinien
CreatorΜανια Αικατερινηel
PublisherInstitute of Electrical and Electronics Engineersen
Content SummaryComfortable, high-quality 3D stereo viewing is becoming a requirement for interactive applications today. Previous research shows that manipulating disparity can alleviate some of the discomfort caused by 3D stereo, but it is best to do this locally, around the object the user is gazing at. The main challenge is thus to develop a gaze predictor in the demanding context of real-time, heavily task-oriented applications such as games. Our key observation is that player actions are highly correlated with the present state of a game, encoded by game variables. Based on this, we train a classifier to learn these correlations using an eye-tracker which provides the ground-truth object being looked at. The classifier is used at runtime to predict object category - and thus gaze - during game play, based on the current state of game variables. We use this prediction to propose a dynamic disparity manipulation method, which provides rich and comfortable depth. We evaluate the quality of our gaze predictor numerically and experimentally, showing that it predicts gaze more accurately than previous approaches. A subjective rating study demonstrates that our localized disparity manipulation is preferred over previous methods.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2018-10-02-
Date of Publication2016-
SubjectGaze predictionen
SubjectPerceptionen
SubjectStereo gradingen
Bibliographic CitationG. A. Koulieris, G. Drettakis, D. Cunningham and K. Mania, "Gaze prediction using machine learning for dynamic stereo manipulation in games," in 18th IEEE Virtual Reality Conference, 2016, pp. 113-120. doi: 10.1109/VR.2016.7504694en

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