Panoramic Video Techniques for Improving Presence in Virtual Environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Photo-realistic techniques that use sequences of images captured from a real environment can be used to create virtual environments (VEs). Unlike 3D modelling techniques, the required human work and computation are independent of the amounts of detail and complexity that exist in the scene, and in addition they provide great visual realism. In this study we created virtual environments using three different photo-realistic techniques: panoramic video, regular video, and a slide show of panoramic still images. While panoramic video offered continuous movement and the ability to interactively change the view, it was the most expensive and time consuming to produce among the three techniques. To assess whether the extra effort needed to create panoramic video is warranted, we analysed how effectively each of these techniques supported a sense of presence in participants. We analysed participants' subjective sense of presence in the context of a navigation task where they travelled along a route in a VE and tried to learn the relative locations of the landmarks on the route. Participants' sense of presence was highest in the panoramic video condition. This suggests that the effort in creating panoramic video might be warranted whenever high presence is desired.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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