Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.021 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".