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Record W4206257736 · doi:10.1145/1073368.1073397

Animosaics

2005· article· en· W4206257736 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 institutionsMcGill University
Fundersnot available
KeywordsComputer scienceAnimationComputer graphics (images)Coherence (philosophical gambling strategy)Computer visionTileProcess (computing)Voronoi diagramFrame (networking)Artificial intelligenceMotion (physics)MosaicGeometryMathematics

Abstract

fetched live from OpenAlex

Animated mosaics are a traditional form of stop-motion animation created by arranging and rearranging small objects or tiles from frame to frame. While this animation style is uniquely compelling, the traditional process of manually placing and then moving tiles in each frame is time-consuming and labourious. Recent work has proposed algorithms for static mosaics, but generating temporally coherent mosaic animations has remained open. In addition, previous techniques for temporal coherence allow non-photorealistic primitives to layer, blend, deform, or scale, techniques that are unsuitable for mosaic animations. This paper presents a new approach to temporal coherence and applies this to build a method for creating mosaic animations. Specifically, we characterize temporal coherence as the coordinated movement of groups of primitives. We describe a system for achieving this coordinated movement to create temporally coherent geometric packings of 2D shapes over time. We also show how to create static mosaics comprised of different tile shapes using area-based centroidal Voronoi diagrams.

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: Methods · Consensus signal: none
Teacher disagreement score0.919
Threshold uncertainty score0.132

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 head0.285
Teacher spread0.268 · 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