Proceedings of the 7th International Symposium on Non-Photorealistic Animation and Rendering
Bibliographic record
Abstract
Welcome to NPAR 2009, the seventh International Symposium on Non-Photorealistic Animation and Rendering. As in 2007, this symposium is co-located with ACM SIGGRAPH, and takes place August 1--2, 2009, in New Orleans, Louisiana, USA. Non-photorealistic animation and rendering (NPAR) refers to techniques for visually communicating ideas and information. Such techniques usually generate imagery which is expressive, rather than photorealistic. The papers presented in this volume showcase new research on both the mechanisms of non-photorealistic animation and rendering techniques as well as principles of visual communication via such artistic animation and rendering. This research area continues to show great promise, as evidenced by the growing use of non-photorealistic techniques in film and games. This year we received 21 paper submissions, each of which was assigned to four members of the program committee. Papers for which one of the program chairs had a conflict were assigned and completely handled by the other program chair. After the end of the reviewing period, the reviewers of each paper discussed the reviews and ultimately came to a consensus on accepting or rejecting the paper. Several papers were accepted conditionally, in which case the authors were given the chance to address some required changes suggested by the reviewers. The outcome of this process is the set of seven papers that are collected in this volume.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.141 | 0.045 |
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".