SGaze: A Data-Driven Eye-Head Coordination Model for Realtime Gaze Prediction

• Published in: IEEE transactions on visualization and computer graphics Vol. 25; no. 5; pp. 2002 - 2010
• Main Authors: Hu, Zhiming, Zhang, Congyi, Li, Sheng, Wang, Guoping, Manocha, Dinesh
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adolescent; Adult; Angular velocity; Computational modeling; Computer Graphics; Coordination models; Databases, Factual; Eye movements; Eye Movements - physiology; eye tracking; Eye-head coordination; Female; gaze prediction; Head; Head movement; Head Movements - physiology; Humans; Imaging, Three-Dimensional; Male; Mathematical models; Navigation; Pearsons correlation coefficient; Predictive models; Real time; Rendering (computer graphics); saliency; Solid modeling; Statistical analysis; User-Computer Interface; Virtual Reality; Visualization; Young Adult
• ISSN: 1077-2626; 1941-0506
• DOI: 10.1109/TVCG.2019.2899187

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.