FACIAL ANIMATION WITH MOTION CAPTURE BASED ON SURFACE BLENDING
Bibliographic record
Abstract
This paper proposes a methodology to reconstruct 3D facial expressions with motion capture data. Featurepoint based facial animation provides easy control of expression usually by moving surface points using mathematical deformation. However it does not support the high quality of surface animation of the face where the feature points are not present. In this paper, we focus on animating a 3D facial model using only feature points, but keeping the high quality animation by using an expression databank obtained from surface scanning. Firstly, a facial expression databank is prepared by processing the raw laser-scanned human face data with a consistent parameterization technique. Secondly, sparse motion capture data is obtained using an optical tracking system. Thirdly, guided by the captured MPEG-4 feature point motions, we find the corresponding surface information in the existing examples in the databank by linear combination of them. The optimized blending weights are obtained implicitly by Genetic Algorithms (GA). Finally, the surface blending result is retargeted into the performer’s neutral facial mesh. Consequently, motions of the facial surface points are reconstructed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".