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Digital Facial Augmentation for Interactive Entertainment

2015· article· en· W2045793747 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

VenueICST Transactions on e-Education and e-Learning · 2015
Typearticle
Languageen
FieldComputer Science
TopicFace recognition and analysis
Canadian institutionsMcGill University
FundersUniversity of Tsukuba
KeywordsAugmented realityComputer scienceComputer visionComputer graphics (images)Artificial intelligenceEntertainmentObject (grammar)Face (sociological concept)Virtual realityProjection (relational algebra)Position (finance)Tracking (education)

Abstract

fetched live from OpenAlex

Digital projection technology allows for effective and entertaining spatial augmented reality applications. Leveraging the capabilities of reasonably accurate object tracking using commodity cameras and/or depth sensors to determine the 3D position and pose of objects in real time, it is possible to project dynamic graphical content on arbitrary surfaces, such as a person’s face. Coupling these capabilities with a simple drawing application, participants can have the experience of "painting" on someone’s face, or even on their own, by observing the projection in a mirror. Similarly, integrating 2D rigid-body, fluid and gravity simulation, one may interact with virtual objects projected on their own face or body.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.377

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 communication0.0000.001
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 head0.018
GPT teacher head0.280
Teacher spread0.262 · 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