Panoramic Video Techniques for Improving Presence in Virtual Environments
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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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".