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DTSTAMP:20260817T171531Z
LOCATION:Hall B5 (1)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241206T151300
DTEND;TZID=Asia/Tokyo:20241206T152700
UID:siggraphasia_SIGGRAPH Asia 2024_sess148_papers_489@linklings.com
SUMMARY:Identifying Behavioral Correlates to Visual Discomfort
DESCRIPTION:David Tovar (Reality Labs, Meta; Vanderbilt University); James
  Wilmott and Xiuyun Wu (Reality Labs, Meta); Daniel Martin (Reality Labs R
 esearch, Meta; Universidad de Zaragoza); and Michael Proulx, Dave Lindberg
 , Yang Zhao, Olivier Mercier, and Phillip Guan (Reality Labs Research, Met
 a)\n\nOutside of self-report surveys, there are no proven, reliable method
 s to quantify visual discomfort or visually induced motion sickness sympto
 ms when using head-mounted displays. While valuable tools, self-report sur
 veys suffer from potential biases and low sensitivity due to variability i
 n how respondents may assess and report their experience. Consequently, ex
 treme visual-vestibular conflicts are generally used to induce discomfort 
 symptoms large enough to measure reliably with surveys (e.g., stationary p
 articipants riding virtual roller coasters). An emerging area of research 
 is the prediction of discomfort survey results from physiological and beha
 vioral markers. However, the signals derived from experiences that are exp
 licitly designed to be uncomfortable may not generalize to more naturalist
 ic experiences where comfort is prioritized. In this work we introduce a c
 ustom VR headset designed to introduce significant near-eye optical distor
 tion (i.e., pupil swim) to induce visual discomfort during more typical VR
  experiences. We evaluate visual comfort in our headset while users play t
 he popular VR title Job Simulator and show that eye-tracked dynamic distor
 tion correction improves visual comfort in a multi-session, within-subject
 s user study. We additionally use representational similarity analysis to 
 highlight changes in head and gaze behavior that are potentially more sens
 itive to visual discomfort than surveys.\n\nRegistration Category: Full Ac
 cess, Full Access Supporter\n\nLanguage Format: English Language\n\nSessio
 n Chair: Peng Song (Singapore University of Technology and Design (SUTD))\
 n\n
URL:https://asia.siggraph.org/2024/program/?id=papers_489&sess=sess148
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