Fire pattern analysis and synthesis using EigenFires and motion transitions
Bibliographic record
Abstract
ABSTRACT We introduce novel approaches of intuitive and easy‐to‐use realistic fire animation, starting from real‐life fire by image‐based techniques and statistical analysis. The results can be utilized as a pre‐rendered sequence of images in video games, motion graphics, and cinematic visual effects. Instead of physics‐based simulation, we employ an example‐based principal component analysis and introduce “EigenFires.” We visualize the main features of various fire samples to analyze their tracks and synthesize a new fire by combining various fire samples, recorded with high frame rates, in order to edit given sequences of fire animations. For this purpose, we present how to recognize similarity of the shapes of fire in order to change the pattern from one style of fire to another distinct style of fire procedurally. Our techniques require very little parameter tuning, compared with conventional physically based fire synthesis, video textures, and dynamic textures. A similar level of visually pleasing compressed fire is also easily produced by using principal component analysis techniques. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".