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Record W2103030280 · doi:10.1162/leon.2010.43.1.34

A Novel Use of 3D Motion Capture: Creating Conceptual Links between Technology and Representation of Human Gesture in the Visual Arts

2010· article· en· W2103030280 on OpenAlexafffund
Gongbing Shan, Peter Visentin, Tanya Harnett

Bibliographic record

VenueLeonardo · 2010
Typearticle
Languageen
FieldEngineering
TopicHuman Motion and Animation
Canadian institutionsUniversity of Lethbridge
FundersAlberta Foundation for the ArtsWestern Canada Research Grid
KeywordsGestureRepresentation (politics)Movement (music)Motion (physics)Computer scienceMotion captureHuman–computer interactionTrajectorySpace (punctuation)Function (biology)Artificial intelligenceCognitive scienceComputer visionCommunicationAestheticsArtSociologyPsychology

Abstract

fetched live from OpenAlex

As an unfolding of time-based events, gesture is intrinsically integrated with the aesthetic experience and function of the human form. In historical and contemporary visual culture, various approaches have been used to communicate the substance of human movement, including use of science and technology. This paper links the understanding of human gesture with technologies influencing its representation. Three-dimensional motion capture permits the accurate recording of movement in 3D computer space and provides a new means of analyzing movement qualities and characteristics. Movement signatures can be related to the human form by virtue of trajectory qualities and experientially and/or culturally dependent interactions.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.006
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.035
GPT teacher head0.285
Teacher spread0.250 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2010
Admission routes2
Has abstractyes

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