SGaze: A Data-Driven Eye-Head Coordination Model for Realtime Gaze Prediction
Zhiming Hu, Congyi Zhang, Sheng Li, Guoping Wang, Dinesh Manocha
IEEE Transactions on Visualization and Computer Graphics (TVCG), 25(5), pp. 2002–2010, 2019.
Abstract
We present a novel, data-driven eye-head coordination model that can be used for realtime gaze prediction for immersive HMD-based applications without any external hardware or eye tracker. Our model (SGaze) is computed by generating a large dataset that corresponds to different users navigating in virtual worlds with different lighting conditions. We perform statistical analysis on the recorded data and observe a linear correlation between gaze positions and head rotation angular velocities. We also find that there exists a latency between eye movements and head movements. SGaze can work as a software-based realtime gaze predictor and we formulate a time related function between head movement and eye movement and use that for realtime gaze position prediction. We demonstrate the benefits of SGaze for gaze-contingent rendering and evaluate the results with a user study.Links
doi: 10.1109/TVCG.2019.2899187
Paper: hu19_tvcg.pdf
BibTeX
@article{hu19_tvcg,
title = {SGaze: A Data-Driven Eye-Head Coordination Model for Realtime Gaze Prediction},
author = {Hu, Zhiming and Zhang, Congyi and Li, Sheng and Wang, Guoping and Manocha, Dinesh},
journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG)},
volume = {25},
number = {5},
pages = {2002--2010},
year = {2019},
doi = {10.1109/TVCG.2019.2899187}
}