RadarVR: Exploring Spatiotemporal Visual Guidance in Cinematic VR
Bibliographic record
Abstract
In cinematic VR, viewers can only see a limited portion of the scene at any time. As a result, they may miss important events outside their field of view. While there are many techniques which offer spatial guidance (where to look), there has been little work on temporal guidance (when to look). Temporal guidance offers viewers a look-ahead time and allows viewers to plan their head motion for important events. This paper introduces spatiotemporal visual guidance and presents a new widget, RadarVR, which shows both spatial and temporal information of regions of interest (ROIs) in a video. Using RadarVR, we conducted a study to investigate the impact of temporal guidance and explore trade-offs between spatiotemporal and spatial-only visual guidance. Results show spatiotemporal feedback allows users to see a greater percentage of ROIs, with 81% more seen from their initial onset. We discuss design implications for future work in this space.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".