Interactive Theater Experience Design Based on Image Recognition in Virtual Reality Environments
Bibliographic record
Abstract
With the advancement of Virtual Reality (VR) technology, interactive theater has gained increasing attention as an emerging art form.VR environments provide audiences with immersive experiences, allowing them not only to observe but also to influence the narrative progression.However, existing research primarily focuses on static scenes and simple interaction mechanisms, lacking real-time analysis of dynamic user behavior, which limits engagement and the quality of the experience.Moreover, traditional image recognition techniques often fall short in accuracy and real-time performance when handling complex scenes, making them insufficient for the evolving demands of interactive theater.Therefore, exploring interactive theater experience design based on image recognition-particularly with adaptive initial contouring and saliency detection-becomes crucial.This study aims to enhance the user experience in interactive theater through two main components.First, it investigates adaptive initial contouring of VR images in interactive theater to enable personalized user interactions.Second, it employs superpixel-based contour-aware methods for saliency detection in VR images, aiming to improve the efficiency and accuracy of visual content recognition.Through these studies, this research seeks to provide new technical support and theoretical foundations for creating interactive theater in VR, driving further advancements in the field.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".