Viewpoint Snapping to Reduce Cybersickness in Virtual Reality
Why this work is in the frame
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Bibliographic record
Abstract
Cybersickness in virtual reality (VR) is an on-going problem, despite recent advances in technology. In this paper, we propose a method for reducing the likelihood of cybersickness onset when using stationary (e.g., seated) VR setups. Our approach relies on reducing optic flow via inconsistent displacement - the viewpoint is “snapped” during fast movement that would otherwise induce cybersickness. We compared our technique, which we call viewpoint snapping, to a control condition without viewpoint snapping, in a custom-developed VR first-person shooter game. We measured participant cybersickness levels via the Simulator Sickness Questionnaire (SSQ), and user reported levels of nausea, presence, and objective error rate. Overall, our results indicate that viewpoint snapping significantly reduced SSQ reported cybersickness levels by about 40% and resulted in a reduction in participant nausea levels, especially with longer VR exposure. Presence levels and error rate were not significantly different between the viewpoint snapping and the control condition.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it