Mapping Real World Locomotion Speed to the Virtual World in Large Field of View Virtual Environments
Bibliographic record
Abstract
In virtual environments, tracking physical movements in the real world and mapping them to movement in a virtual world increases immersion and the experience of presence. For example, walking on a treadmill in the physical world may be mapped to camera movement in a first-person view of the virtual world. However, due to interrelated factors relating to the field of view and distortion of objects in the virtual environment, matching physical movement speed to virtual world movement speed world so that it ‘feels right’ to a user can be complex. This perceived mismatch is detrimental as it can induce motion sickness and reduce the experience of presence. Although previously investigated with head-mounted displays, there is little information about how to overcome this mismatch when using large 2D screens that provide a very different viewing environment. To address this gap, we investigate how a 180-degree display that nearly fills the entire human FOV impacts this perceptual mismatch while walking and running on a treadmill. Our results show that people prefer camera speeds that actually exceed their physical movement speed, and increasingly so at higher speeds. Interestingly, though, people’s tolerance for deviations from the ideal camera speed mapping does not change with movement speed. We propose a simple personalized linear model that can be quickly calibrated for a user to provide the best match. This work provides important findings to inform and improve the design of virtual environments for an improved user experience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".