Proceedings of the 2008 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Bibliographic record
Abstract
The ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA) is the leading meeting devoted exclusively to computer animation. The symposium attracts high quality papers and provides an opportunity for researchers in the area of computer animation to meet, share ideas and discuss emerging directions in the field. This year the eighth SCA was held in historic Dublin, Ireland, with special accommodations at Trinity College. These proceedings contain 24 papers from 60 submissions. The exceptional quality of the submitted papers made the final selection difficult. Each paper was typically reviewed by at least 4 members of the international program committee (IPC). The acceptance decisions involved sometimes lengthy online discussions among the IPC members. Similar to previous years, the final program includes papers on a variety of topics, including fluids, deformable objects, behavior modeling, real-time simulation, and motion capture and planning. This year the proceedings is in full color. The symposium continues to highlight promising works in progress with a poster and demo sessions. Our program included posters and demos which were presented in a special reception. Some of the posters were submitted papers that were not selected by the papers program, but encouraged to appear as posters. Additional posters were selected through a separate poster-review process.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.098 | 0.036 |
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".