Modeling and animating for the dense laser-scanned face in the low resolution level
Bibliographic record
Abstract
Modeling the human face and producing realistic facial animation are the challenging tasks for computer animators. On the other hand, with the development of advanced laser-scanning service, it is capable of capturing face with millions of triangles. In the situations where the real-time animation is expected, the problem of how to reduce the size of the dense laser-scanned face data for the animation purpose has been addressed. In this paper, firstly we present an approach that is capable of producing the low polygon approximation model for the dense laser-scanned face while accurately conveying the distinguished features in the original data. We modify the predefined generic model based on the feature points to produce the approximation model. The modification of the generic model involves three steps: Radial Basis Function (RBF) morphing; then loop subdivision step followed by mesh refinement. Secondly, instead of creating new facial animation from scratch, we take advantage of the existing source animation data and use the face motion retargeting method to resample the source motion vectors onto our approximation model. The resulting facial animation is fast and efficient.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".