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Gaze Prediction using Machine Learning for Dynamic Stereo Manipulation in Games

Koulieris, G.A.; Drettakis, G.; Cunningham, D.; Mania, K.

Gaze Prediction using Machine Learning for Dynamic Stereo Manipulation in Games Thumbnail


Authors

G. Drettakis

D. Cunningham

K. Mania



Contributors

Tobias Höllerer
Editor

Victoria Interrante
Editor

Anatole Lécuyer
Editor

Evan Suma
Editor

Abstract

Comfortable, 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.

Citation

Koulieris, G., Drettakis, G., Cunningham, D., & Mania, K. (2016). Gaze Prediction using Machine Learning for Dynamic Stereo Manipulation in Games. In T. Höllerer, V. Interrante, A. Lécuyer, & E. Suma (Eds.), 2016 IEEE Virtual Reality Conference (VR) : Greenville, South Carolina, USA, 19-23 March 2016. Proceedings (113-120). https://doi.org/10.1109/vr.2016.7504694

Conference Name IEEE VR 2016 IEEE
Conference Location Greenville, South Carolina, USA
Start Date Mar 19, 2016
End Date Mar 23, 2016
Acceptance Date Nov 23, 2015
Online Publication Date Jul 7, 2016
Publication Date Jul 7, 2016
Deposit Date Jun 5, 2018
Publicly Available Date Jul 24, 2018
Pages 113-120
Series ISSN 2375-5334
Book Title 2016 IEEE Virtual Reality Conference (VR) : Greenville, South Carolina, USA, 19-23 March 2016. Proceedings.
DOI https://doi.org/10.1109/vr.2016.7504694
Public URL https://durham-repository.worktribe.com/output/1146667

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Supplemental material © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.






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