SGaze: A Data-Driven Eye-Head Coordination Model for Realtime Gaze Prediction
PubDate: February 2019
Teams: Peking University；University of Maryland
Writers: Zhiming Hu; Congyi Zhang; Sheng Li; Guoping Wang; Dinesh Manocha
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.