BEGIN:VCALENDAR VERSION:2.0 PRODID:Linklings LLC BEGIN:VTIMEZONE TZID:Australia/Melbourne X-LIC-LOCATION:Australia/Melbourne BEGIN:DAYLIGHT TZOFFSETFROM:+1000 TZOFFSETTO:+1100 TZNAME:AEDT DTSTART:19721003T020000 RRULE:FREQ=YEARLY;BYMONTH=4;BYDAY=1SU END:DAYLIGHT BEGIN:STANDARD DTSTART:19721003T020000 TZOFFSETFROM:+1100 TZOFFSETTO:+1000 TZNAME:AEST RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260114T163632Z LOCATION:Darling Harbour Theatre\, Level 2 (Convention Centre) DTSTART;TZID=Australia/Melbourne:20231212T093000 DTEND;TZID=Australia/Melbourne:20231212T124500 UID:siggraphasia_SIGGRAPH Asia 2023_sess209_papers_282@linklings.com SUMMARY:The Shortest Route Is Not Always the Fastest: Probability-Modeled Stereoscopic Eye Movement Completion Time in VR DESCRIPTION:Budmonde Duinkharjav and Benjamin Liang (New York University), Anjul Patney and Rachel Brown (NVIDIA Research), and Qi Sun (New York Uni versity)\n\nSpeed and consistency of target-shifting play a crucial role i n human ability to perform complex tasks. Shifting our gaze between object s of interest quickly and consistently requires changes both in depth and direction. Gaze changes in depth are driven by slow, inconsistent vergence movements which rotate the eyes in opposite directions, while changes in direction are driven by ballistic, consistent movements called saccades, w hich rotate the eyes in the same direction. In the natural world, most of our eye movements are a combination of both types. While scientific consen sus on the nature of saccades exists, vergence and combined movements rema in less understood and agreed upon.\n\nWe eschew the lack of scientific co nsensus in favor of proposing an operationalized computational model which predicts the speed of any type of gaze movement during target-shifting in 3D. To this end, we conduct a psychophysical study in a stereo VR environ ment to collect more than 12,000 gaze movement trials, analyze the tempora l distribution of the observed gaze movements, and fit a probabilistic mod el to the data. We perform a series of objective measurements and user stu dies to validate the model. The results demonstrate its predictive accurac y, generalization, as well as applications for optimizing visual performan ce by altering content placement. Lastly, we leverage the model to measure differences in human target-changing time relative to the natural world, as well as suggest scene-aware projection depth. By incorporating the comp lexities and randomness of human oculomotor control, we hope this research will support new behavior-aware metrics for VR/AR display design, interfa ce layout, and gaze-contingent rendering.\n\nRegistration Category: Full A ccess, Enhanced Access, Trade Exhibitor, Experience Hall Exhibitor\n\n URL:https://asia.siggraph.org/2023/full-program?id=papers_282&sess=sess209 END:VEVENT END:VCALENDAR