A workflow for designing stylized shading effects
Bibliographic record
Abstract
In this report, we describe a workflow for designing stylized shading effects on a 3D object, targeted at technical artists. Shading design, the process of making the illumination of an object in a 3D scene match an artist vision, is usually a time-consuming task because of the complex interactions between materials, geometry, and lighting environment. Physically based methods tend to provide an intuitive and coherent workflow for artists, but they are of limited use in the context of non-photorealistic shading styles. On the other hand, existing stylized shading techniques are either too specialized or require considerable hand-tuning of unintuitive parameters to give a satisfactory result. Our contribution is to separate the design process of individual shading effects in three independent stages: control of its global behavior on the object, addition of procedural details, and colorization. Inspired by the formulation of existing shading models, we expose different shading behaviors to the artist through parametrizations, which have a meaningful visual interpretation. Multiple shading effects can then be composited to obtain complex dynamic appearances. The proposed workflow is fully interactive, with real-time feedback, and allows the intuitive exploration of stylized shading effects, while keeping coherence under varying viewpoints and light configurations. Furthermore, our method makes use of the deferred shading technique, making it easily integrable in existing rendering pipelines.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".