A spectral-particle hybrid method for rendering falling snow
Bibliographic record
Abstract
Falling snow has the visual property that it is simultaneously a set of discrete moving particles as well as a dynamic texture. To capture the dynamic texture properties of falling snow using particle systems can, however, require so many particles that it severely impacts rendering rates. Here we address this limitation by rendering the texture properties directly. We use a standard particle system to generate a relatively sparse set of falling snow flakes, and we then composite in a dynamic texture to fill in between the particles. The texture is generated using a novel image-based spectral synthesis method. The spectrum of the falling snow texture is defined by a dispersion relation in the image plane, derived from linear perspective. The dispersion relation relates image speed, image size, and particle depth. In the frequency domain, it relates the wavelength and speed of moving 2D image sinusoids. The parameters of this spectral snow can be varied both across the image and over time. This provides the flexibility to match the direction and speed parameters of the spectral snow to those of the falling particles. Camera motion can also be matched. Our method produces visually pleasing results at interactive rendering rates. We demonstrate our approach by adding snow effects to static and dynamic scenes. An extension for creating rain effects is also presented.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".