Preference galleries for material design
Bibliographic record
Abstract
Properly modeling the appearance of a material is very important for realistic image synthesis. The appearance of a material is formalized by the notion of the Bidirectional Reflectance Distribution Function (BRDF). In computer graphics, BRDFs are most often specified using various analytical models. Analytical models that are of interest to realistic image synthesis are the ones that observe the physical laws of reciprocity and energy conservation while typically also exhibiting shadowing, masking and Fresnel reflectance phenomenon. Realistic models are hence fairly complex with many parameters that need to be adjusted by the designer for the proper material appearance. Unfortunately these parameters can interact in non-intuitive ways, and small adjustments to certain settings may result in non-uniform changes in the appearance. This can make the material design process hard for an artist or a non-expert user. To alleviate this problem, Ngan et al. [2006] recently presented an interface for navigation in a perceptually uniform BRDF space based on a metric derived from user studies. However, this is still somewhat constraining as the user has to develop an understanding of the various aspects of material appearance such as varying degrees of diffuseness, glossiness, specularity, Fresnel effects and/or anisotropy in order to navigate such an interface. An artist or a user often knows the look that he or she desires for a particular application without necessarily being interested in understanding the various subtleties of reflection! This is what we seek to address in this work with a 'preference gallery' approach to material design.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.059 | 0.014 |
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".