Bibliographic record
Abstract
In computer facial animation, consecutive frames are generated to create motion, or expression, on a com-puter modelled face. One facial animation technique, the performance-based animation technique, uses hu-man data, such as a video recording that tracks the features of the face, to drive an animation. The present document describes a performance-based implementation called CASSI or Computer Animated Speech SIm-ulator. CASSI uses human data in the form of 2D X-ray microbeam (XRMB) data to drive the animation of a 3D facial model. The 2D X-ray microbeam data contain coordinate values tracking the side view movement of eight gold pellets placed as follows: one on the upper lip, one on the lower lip, four on the tongue, and two on the jaw. The XRMB data track the movement of the pellets attached to human subjects who are performing speech-related tasks. The 3D facial model is an augmentation of the parameterized facial model developed by Parke. CASSI was implemented as three versions: CASSI 1.0, CASSI 2.0, and CASSI 2.1. CASSI 1.0 was designed to integrate the XRMB data les with Parke's facial model. This integration included initializing the chin, palate, tongue, teeth, and lips of Parke's model, and animating the model, particularly, rotating the jaw, with the XRMB data. The emphasis of CASSI 2.0 was on lip movement, in particular, on rounding
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".