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Authors: Jiao, Chuhan
Hu, Zhiming
Bâce, Mihai
Bulling, Andreas
Title: SUPREYES: SUPer resolution for EYES using implicit neural representation learning
Issue Date: 2023 Konferenzbeitrag ACM Symposium on User Interface Software and Technology (36th, 2023, San Francisco, Calif.) UIST '23 : proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology. New York, NY : ACM, 2023. - ISBN 979-8-4007-0132-0, Article no. 81
ISBN: 979-8-4007-0132-0
Abstract: We introduce SUPREYES - a novel self-supervised method to increase the spatio-temporal resolution of gaze data recorded using low(er)-resolution eye trackers. Despite continuing advances in eye tracking technology, the vast majority of current eye trackers - particularly mobile ones and those integrated into mobile devices - suffer from low-resolution gaze data, thus fundamentally limiting their practical usefulness. SUPREYES learns a continuous implicit neural representation from low-resolution gaze data to up-sample the gaze data to arbitrary resolutions. We compare our method with commonly used interpolation methods on arbitrary scale super-resolution and demonstrate that SUPREYES outperforms these baselines by a significant margin. We also test on the sample downstream task of gaze-based user identification and show that our method improves the performance of original low-resolution gaze data and outperforms other baselines. These results are promising as they open up a new direction for increasing eye tracking fidelity as well as enabling new gaze-based applications without the need for new eye tracking equipment.
Appears in Collections:05 Fakultät Informatik, Elektrotechnik und Informationstechnik

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