Real-time head-based deep-learning model for gaze probability regions in collaborative VR

Bovo, Riccardo and Giunchi, Daniele and Sidenmark, Ludwig and Costanza, Enrico and Gellersen, Hans and Heinis, Thomas (2022) Real-time head-based deep-learning model for gaze probability regions in collaborative VR. In: ACM Symposium on Eye Tracking Research and Applications. ACM, USA. ISBN 9781450392525

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Eye behaviour has gained much interest in the VR research community as an interaction input and support for collaboration. Researchers implemented gaze inference models when eye-tracking is missing by using head behavior and saliency. However, these solutions are resource-demanding and thus unfit for untethered devices, and their angle accuracy is around 7°, which can be a problem in high-density informative areas. To address this issue, we propose a lightweight deep learning model that generates the probability density function of the gaze as a percentile contour. This solution allows us to introduce a visual attention representation based on a region rather than a point and manage a trade-off between the ambiguity of a region and the error of a point. We tested our model in untethered devices with real-time performances; we evaluated its accuracy which outperforms our identified baselines (average fixation map and head direction).

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© ACM, 2022. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ETRA '22: 2022 Symposium on Eye Tracking Research and Applications
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17 May 2022 10:15
Last Modified:
15 Sep 2023 02:07