State of the Art in Artistic Editing of Appearance, Lighting and Material
Bibliographic record
Abstract
Abstract Mimicking the appearance of the real world is a longstanding goal of computer graphics, with several important applications in the feature film, architecture and medical industries. Images with well‐designed shading are an important tool for conveying information about the world, be it the shape and function of a computer‐aided design (CAD) model, or the mood of a movie sequence. However, authoring this content is often a tedious task, even if undertaken by groups of highly trained and experienced artists. Unsurprisingly, numerous methods to facilitate and accelerate this appearance editing task have been proposed, enabling the editing of scene objects' appearances, lighting and materials, as well as entailing the introduction of new interaction paradigms and specialized preview rendering techniques. In this review, we provide a comprehensive survey of artistic appearance, lighting and material editing approaches. We organize this complex and active research area in a structure tailored to academic researchers, graduate students and industry professionals alike. In addition to editing approaches, we discuss how user interaction paradigms and rendering back ends combine to form usable systems for appearance editing. We conclude with a discussion of open problems and challenges to motivate and guide future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".