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Record W1984683508 · doi:10.1145/1878083.1878087

A real-time performance system for virtual theater

2010· article· en· W1984683508 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHuman Motion and Animation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceVirtual realityIllusionMultimediaHuman–computer interactionMotion captureComputer graphics (images)Motion (physics)Artificial intelligence

Abstract

fetched live from OpenAlex

The idea of combining virtual reality technology and theatrical tradition to create virtual plays has captured artists' imaginations for some time. Using conventional technology, the use of virtual characters in a theatrical performance often integrates the predefined animations of virtual actors into the theater scene, resulting in a performance that can feel stilted and unresponsive due to its pre-programmed nature. This paper proposes a new system that allows actors to animate virtual characters in real time, resulting in a more flexible and interactive theatrical performance experience. Actors are sequestered at a remote site, invisible to the audience, and are digitized by a motion capture system. Using camera feeds to provide the remote actors with information about the behavior of the live actors and audience in the theater, the remote actors can adapt their virtual counterparts' behavior to react to live events in real-time, giving the illusion to the audience that the virtual characters are responsive to their actions.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.931
Threshold uncertainty score0.678

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)0.0000.001

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.

Opus teacher head0.006
GPT teacher head0.190
Teacher spread0.184 · 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

Quick stats

Citations28
Published2010
Admission routes1
Has abstractyes

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