A Region-Based Facial Motion Analysis and Retargeting Model for 3D Characters
Bibliographic record
Abstract
With the expanding applicable scenarios of 3D facial animation, abundant research has been done on facial motion capture, 3D face parameterization, and retargeting. However, current retargeting methods still struggle to reflect the source motion on a target 3D face accurately. One major reason is that the source motion is not translated into precise representations of the motion meanings and intensities, resulting in the target 3D face presenting inaccurate motion semantics. We propose a region-based facial motion analysis and retargeting model that focuses on predicting detailed facial motion representations and providing a plausible retargeting result through 3D facial landmark input. We have defined the regions based on facial muscle behaviours and trained a motion-to-representation regression for each region. A refinement process, designed using an autoencoder and a motion predictor for facial landmarks, which works for both real-life subjects' and fictional characters' face rigs, is also introduced to improve the precision of the retargeting. The region-based strategy effectively balances the motion scales of the different facial regions, providing reliable representation prediction and retargeting results. The representation prediction and refinement with 3D facial landmark input have enabled flexible application scenarios such as video-based and marker-based motion retargeting, and the reuse of animation assets for Computer-Generated (CG) characters. Our evaluation shows that the proposed model provides semantically more accurate and visually more natural results than similar methods and the commercial solution from Faceware. Our ablation study demonstrates the positive effects of the region-based strategy and the refinement process.
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.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".