Giunchi, Daniele;
Bovo, Riccardo;
Bhatia, Nitesh;
Heinis, Thomas;
Steed, Anthony;
(2024)
Fovea Prediction Model in VR.
In:
(Proceedings) 31st IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (IEEE VR 2024).
IEEE: Orlando, FL, USA.
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Abstract
We propose a lightweight deep learning approach for gaze estimation that represents the visual field as three distinct regions: fovea, near, and far peripheral. Each region is modelled using a gaze parameterization gaze regarding angle-magnitude, latitude, or a combination of angle-magnitude-latitude. We evaluated how accurately these representations can predict a user's gaze across the visual field when trained on data from VR headsets. Our experiments confirmed that the latitude model generates gaze predictions with superior accuracy with an average latency compatible with the demanding real-time functionalities of an untethered device. We generated an outperforming ensemble model with a comparable latency.
| Type: | Proceedings paper |
|---|---|
| Title: | Fovea Prediction Model in VR |
| Event: | 31st IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (IEEE VR 2024) |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://ieeevr.org/2024/program/posters/#P2 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10190410 |
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