Emotion Manipulation for Talking-Head Videos via Facial Landmarks
Bibliographic record
Abstract
Manipulating the emotion of a performer in a video is a challenging task. The lip motion needs to be preserved while performing the desired changes in the emotion of the subject; however, simply utilizing existing image-based editing methods sabotages the original lip synchronization. We tackle this problem by utilizing a pretrained StyleGAN paired with a landmark-based editing module that modifies the bias present in the edit direction used in image manipulation. The proposed editing module consists of a latent-based landmark detection network and an editing network that modifies the editing direction to match the original lip synchronization while preserving the desired emotion manipulation results. This is realized by taking the facial landmarks as control points. Both networks operate on the latent space, which enables fast training and inference. We show that the proposed method runs significantly faster and performs better in terms of visual quality than alternative approaches, which was validated through a perceptual study. The proposed method can also be extended to perform face reenactment to generate a talking-head video from a single image and face image manipulation using facial landmarks as control points.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".