ViCoFace: Learning Disentangled Latent Motion Representations for Visual-Consistent Face Reenactment
Bibliographic record
Abstract
Unsupervised face reenactment aims to animate a source image to imitate the motions of a target image while retaining the source portrait’s attributes like facial geometry, identity, hair texture, and background. While prior methods can extract the motion from the target image via compact representations (e.g., keypoints or latent motion bases [ 50 ]), they are not robust in predicting motions that are disentangled with portrait attributes, thus failing to preserve portrait attributes in the cross-subject reenactment. In this work, we propose an effective and cost-efficient face reenactment approach to address this issue. Our approach is highlighted by two major strengths. First, based on the theory of latent motion bases, we disentangle the full-head motion into two parts: the transferable motion and preservable motion and then compose the full motion representation using latent motions from the source image and the target image. Second, to optimize and learn disentangled motions, we introduce an efficient training framework, which features two training strategies: (1) a mixture training strategy that encompasses self-reenactment training and cross-subject training for better motion disentanglement and (2) a multi-path training strategy that improves the visual consistency of portrait attributes. Extensive experiments on widely used benchmarks demonstrate that our method exhibits a remarkable generalization ability compared to state-of-the-art baselines. Project and demos are available at https://junleen.github.io/projects/vicoface .
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".