Personalized Visual Dubbing through Virtual Dubber and Full Head Reenactment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Visual dubbing aims to modify facial expressions to ''lip-sync'' a new audio track. While person-generic talking head generation methods achieve expressive lip synchronization across arbitrary identities, they usually lack person-specific details and fail to generate high-quality results. Conversely, person-specific methods require extensive training. Our method combines the strengths of both methods by incorporating a virtual dubber, a person-generic talking head, as an intermediate representation. We then employ an autoencoder-based person-specific identity swapping network to transfer the actor identity, enabling fullhead reenactment that includes hair, face, ears, and neck. This eliminates artifacts while ensuring temporal consistency. Our quantitative and qualitative evaluation demonstrate that our method achieves a superior balance between lip-sync accuracy and realistic facial reenactment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.000 | 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