SUPREYES: SUPer Resolution for EYES Using Implicit Neural Representation Learning
Chuhan Jiao, Zhiming Hu, Mihai Bâce, Andreas Bulling
Proc. ACM Symposium on User Interface Software and Technology (UIST), pp. 1–13, 2023.
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.Links
Paper: jiao23_uist.pdf
BibTeX
@inproceedings{jiao23_uist,
author = {Jiao, Chuhan and Hu, Zhiming and B{\^a}ce, Mihai and Bulling, Andreas},
title = {SUPREYES: SUPer Resolution for EYES Using Implicit Neural Representation Learning},
booktitle = {Proc. ACM Symposium on User Interface Software and Technology (UIST)},
year = {2023},
pages = {1--13},
doi = {10.1145/3586183.3606780}
}