Perceptually valid facial expression blending using expression units
Bibliographic record
Abstract
The human face is a rich source of information regarding underlying emotional states. Facial expressions are crucial in showing the emotions as well as increasing the quality of communication and speech comprehension. The detailed study of facial actions involved in the expression of the six universal emotions [1] has helped the computer graphics community develop realistic facial animations. Yet the visual mechanisms by which these facial expressions are altered or combined to convey more subtle information remains less well understood by behavioural psychologists and animators. This lack of a strong theoretical basis for combining facial actions has resulted in the use of ad-hoc methods for blending facial expression in animations [2--3]. They mainly consider the facial movements for transient or combined expressions a simple mathematical function of the main expressions involved. The methods that have emerged are therefore computationally tractable, but the question of their "perceptual" and "psychological" validity has not yet been answered. Examples of such methods are "Sum of two expressions with or without limits," "Weighted averaging," and "MAX operator".
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.040 | 0.001 |
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; both teacher heads agree on what is shown here.
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".