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231225s2019 xx |||||o 00| ||eng c |
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|a 10.1109/TVCG.2019.2899187
|2 doi
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|a pubmed24n0980.xml
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|a eng
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| 100 |
1 |
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|a Hu, Zhiming
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a SGaze
|b A Data-Driven Eye-Head Coordination Model for Realtime Gaze Prediction
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| 264 |
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|c 2019
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| 336 |
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Completed 10.02.2020
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| 500 |
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|a Date Revised 10.02.2020
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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| 520 |
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|a We present a novel, data-driven eye-head coordination model that can be used for realtime gaze prediction for immersive HMD-based applications without any external hardware or eye tracker. Our model (SGaze) is computed by generating a large dataset that corresponds to different users navigating in virtual worlds with different lighting conditions. We perform statistical analysis on the recorded data and observe a linear correlation between gaze positions and head rotation angular velocities. We also find that there exists a latency between eye movements and head movements. SGaze can work as a software-based realtime gaze predictor and we formulate a time related function between head movement and eye movement and use that for realtime gaze position prediction. We demonstrate the benefits of SGaze for gaze-contingent rendering and evaluate the results with a user study
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Zhang, Congyi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Sheng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Guoping
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Manocha, Dinesh
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 25(2019), 5 vom: 18. Mai, Seite 2002-2010
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|u http://dx.doi.org/10.1109/TVCG.2019.2899187
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