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Record W1848410221 · doi:10.11575/prism/30500

Expressive Distortion of Strokes and 3D Meshes

2005· article· en· W1848410221 on OpenAlexafffund
Petra Neumann, Sheelagh Carpendale

Bibliographic record

VenuePRISM (University of Calgary) · 2005
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolygon meshComputer scienceUsabilityDistortion (music)Pipeline (software)Geometry processingOrientation (vector space)Computer graphics (images)Computational scienceArtificial intelligenceHuman–computer interactionGeometryProgramming languageMathematics

Abstract

fetched live from OpenAlex

We introduce methods for applying two-dimensional distortions in a manner that allows us to create three-dimensional variations of stroke-based models and 3D polygonal meshes. These variations can be achieved interactively, supporting the creation of expressive variations of a given 3D model or of strokes generated from it. In order to allow for a seamless integration into existing systems and to enable additional processing of both strokes and meshes, a pipeline approach is taken in both cases. In addition, we discuss the interaction possibilities arising from the interplay between the model orientation and the strokes or the geometry. Finally, to demonstrate the usability of the proposed methods we suggest several application domains.

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: Methods · Consensus signal: none
Teacher disagreement score0.875
Threshold uncertainty score0.234

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 head0.009
GPT teacher head0.212
Teacher spread0.203 · 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
GenreMethods

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
Published2005
Admission routes2
Has abstractyes

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