S3: Speech, Script and Scene driven Head and Eye Animation
Bibliographic record
Abstract
We present S 3 , a novel approach to generating expressive, animator-centric 3D head and eye animation of characters in conversation. Given speech audio, a Directorial script and a cinematographic 3D scene as input, we automatically output the animated 3D rotation of each character's head and eyes. S 3 distills animation and psycho-linguistic insights into a novel modular framework for conversational gaze capturing: audio-driven rhythmic head motion; narrative script-driven emblematic head and eye gestures; and gaze trajectories computed from audio-driven gaze focus/aversion and 3D visual scene salience. Our evaluation is four-fold: we quantitatively validate our algorithm against ground truth data and baseline alternatives; we conduct a perceptual study showing our results to compare favourably to prior art; we present examples of animator control and critique of S 3 output; and present a large number of compelling and varied animations of conversational gaze.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".