A review of motion retargeting techniques for 3D character facial animation
Bibliographic record
Abstract
3D face animation has been a critical component of character animation in a wide range of media since the early 90’s. The conventional process for animating a 3D face is usually keyframe-based, which is labor-intensive. Therefore, the film and game industries have started using live-action actors’ performances to animate the faces of 3D characters, the process is also known as performance-driven facial animation. At the core of performance-driven facial animation is facial motion retargeting, which transfers the source facial motions to a target 3D face. However, facial motion retargeting still has many limitations that influence its capability to further assist the facial animation process. Existing motion retargeting frameworks cannot accurately transfer the source motion’s semantic information (i.e., meaning and intensity of the motion), especially when applying the motion to non-human-like or stylized target characters. The retargeting quality relies on the parameterization of the target face, which is time-consuming to build and usually not generalizable across proportionally different faces. In this survey paper, we review the literature relating to 3D facial motion retargeting methods and the relevant topics within this area. We provide a systematic understanding of the essential modules of the retargeting pipeline, a taxonomy of the available approaches under these modules, and a thorough analysis of their advantages and limitations with research directions that could potentially contribute to this area. We also contributed a 3D character categorization matrix, which has been used in this survey and might be useful for future research to evaluate the character compatibility of their retargeting or face parameterization methods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".