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The Application of VR in the Film Industry

2024· article· en· W4405121213 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueApplied and Computational Engineering · 2024
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsAurora College
Fundersnot available
KeywordsNarrativeVirtual realityTransformative learningStorytellingComputer scienceHuman–computer interactionMultimediaPsychologyArt

Abstract

fetched live from OpenAlex

The integration of Virtual Reality (VR) technology into the film industry has ushered in a new era of immersive and interactive storytelling that fundamentally alters the way narratives are constructed and experienced. This paper explores the transformative impact of VR on narrative structures, multisensory experiences, and the myriad challenges filmmakers face in creating engaging and coherent VR films. By enabling dynamic environmental changes and personalized narrative paths, VR films significantly enhance user engagement and foster a deeper emotional connection between the audience and the story. The paper also delves into the potential of multisensory experiences to enrich learning, therapy, and training, highlighting the promise and pitfalls of current VR technology. We conclude by emphasizing the potential of VR to revolutionize cinema, suggesting that as the technology matures, it will lead to a fundamental shift in how we interact with and experience films.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.129

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.235
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it