SUPREYES: SUPer resolution for EYES using implicit neural representation learning

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.