Talking face: using facial feature detection and image transformations for visual speech
Why this work is in the frame
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Bibliographic record
Abstract
Visual presentation of a talking person requires the generation of image frames showing the speaker in various views while pronouncing various phonemes. The existing approaches, mostly use either a complex 3D geometric model to reconstruct a desired image or a set of 2D images for each viewpoint, to select from. We propose a new system which utilizes facial feature detection and image-based transformation to create any talking frame using only one given image from the desired viewpoint and a set of reference images from one standard view. The proposed approach, together with optical flow-based view morphing and a customizable concatenative text-to-speech, makes a personalized visual speech generation system which can be used for moving/talking head applications where an optimal trade-of between computational complexity and image database requirements is necessary.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it