RUDN University improves eye tracking technology in VR systems

January 26, 2021 //By Jean-Pierre Joosting
RUDN University improves eye tracking technology in VR systems
A mathematical modelling method helps calculate next gaze fixation points in advance for accurate foveated rendering in VR systems.

A team from MSU together with a professor from RUDN University have developed a mathematical model that helps accurately predict the next gaze fixation point and reduces the inaccuracy caused by blinking. The model would make VR/AR systems more realistic and sensitive to user actions.

When a person looks at something, their gaze is focused on the so-called foveated region, and everything else is covered by peripheral vision. In VR systems, a computer has to render the images in the foveated region with the highest degree of detail, while other parts require less computational powers. This approach helps improve computational performance and eliminates issues caused by the gap between the limited capabilities of graphic processors and increasing display resolution. However, foveated rendering technology is limited in speed and accuracy of the next gaze fixation point prediction because the movement of a human eye is a complex and largely random process. To solve this issue, a team of researchers from MSU together with a professor from RUDN University developed a mathematical modelling method that helps calculate next gaze fixation points in advance.

"One of the issues with foveated rendering is timely prediction of the next gaze fixation point because vision is a complex stochastic process. We suggested a mathematical model that predicts gaze fixation point changes," said Prof. Viktor Belyaev, a Ph.D. in Technical Sciences from the Department of Mechanics and Mechatronics of RUDN University.


The tracking of eye movement is one of the key elements of virtual and amplified reality technologies (VR/AR). A team from MSU together with a professor from RUDN University developed a mathematical model that helps accurately predict the next gaze fixation point and reduces the inaccuracy caused by blinking. The model would make VR/AR systems more realistic and sensitive to user actions. Image courtesy of RUDN University.


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