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Record W1503886055 · doi:10.20380/gi2001.07

Simplification and Real-time Smooth Transitions of Articulated Meshes

2001· article· en· W1503886055 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
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMorphingPolygon meshRepresentation (politics)Computer scienceSet (abstract data type)Metric (unit)Computer graphics (images)Theoretical computer scienceAnimationAlgorithmSolid modelingArtificial intelligenceProgramming languageEngineering

Abstract

fetched live from OpenAlex

Simplification techniques have mainly been applied on static models. However in movie and game industries, many models are designed to be animated. We extend the progressive mesh technique to handle skeletally-articulated meshes in order to obtain a continuous level-of-detail (CLOD) representation that retains its ability to be animated. Our technique is not limited to any simplification metric, nor is it limited to generating models composed of a subset of the original vertices. It thus preserves the full simplification potential. To further improve performance, we can use this CLOD representation and extract a discrete set of skeletally-articulated models. Each model can be independently optimized, such as by using triangle strips. We can also use morphing between the different models in order to create smoother transitions. The result is a more accurate representation of animated articulated models, suitable for real-time applications.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.917
Threshold uncertainty score0.189

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 head0.015
GPT teacher head0.270
Teacher spread0.255 · 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