MétaCan
Menu
Back to cohort
Record W4239149143 · doi:10.22215/etd/2014-10231

Perceptually Guided Processing of Style and Affect in Human Motion for Multimedia Applications

2014· dissertation· en· W4239149143 on OpenAlexaff
Seyed Etemad

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicHuman Motion and Animation
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceAnimationMotion (physics)PerceptionArtificial intelligenceHuman–computer interactionProcess (computing)Interface (matter)Set (abstract data type)Computer animationComputer visionComputer graphics (images)

Abstract

fetched live from OpenAlex

Computer graphics and animation, as a direct result of advancements in hardware and software, have become broad and demanding areas of research. Animation of human motion is a major component in the field that has attracted many due to its significance in movies, games, and virtual environments. We propose that processing features for style and affect, which are fundamental determinants of personality and naturally appearing motion, should be carried out through perceptually guided processing techniques. In this dissertation, we employ this approach and develop a set of tools for extraction, synthesis, and analysis of affective and stylistic motion features.

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 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: Empirical
Teacher disagreement score0.786
Threshold uncertainty score0.536

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 head0.017
GPT teacher head0.294
Teacher spread0.277 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicHuman Motion and AnimationFrench-language works237,207