Bibliographic record
Abstract
Abstract We present a technique for creating a smoothly varying sequence of procedural textures that interpolates between arbitrary input samples of texture. This texture transformation uses a library of procedural shaders and selects the correct shaders and associated parameters to accomplish the task. In general, selecting a procedural texture from a library, or finding the correct parameters to produce a smooth texture transition can be complex and time consuming. We propose a strategy for automating this process. While superficially this problem appears intractable for both humans and computational systems, its natural characteristics make a computational solution feasible. We present an algorithm and experimental results demonstrating this approach. Transformation between two textures can then be achieved procedurally, while enforcing perceptual similarity constraints between adjacent texture frames. We describe a technique for efficiently sampling the parameter domain of a shader based on a texture similarity function to create a smooth path through its texture range. In the case of evolving between several shaders, a method is described to obtain the best jump‐points which can be used to connect different shaders smoothly in texture space. Several examples of the technique are shown, and future directions as well as potential problems are discussed. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Texture
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".