Synthesizing waves from animated height fields
Bibliographic record
Abstract
Computer animated ocean waves for feature films are typically carefully choreographed to match the vision of the director and to support the telling of the story. The rough shape of these waves is established in the previsualization (previs) stage, where artists use a variety of modeling tools with fast feedback to obtain the desired look. This poses a challenge to the effects artists who must subsequently match the locked-down look of the previs waves with high-quality simulated or synthesized waves, adding the detail necessary for the final shot. We propose a set of automated techniques for synthesizing Fourier-based ocean waves that match a previs input, allowing artists to quickly enhance the input wave animation with additional higher-frequency detail that moves consistently with the coarse waves, tweak the wave shapes to flatten troughs and sharpen peaks if desired (as is characteristic of deep water waves), and compute a physically reasonable velocity field of the water analytically. These properties are demonstrated with several examples, including a previs scene from a visual effects production environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".