Modeling and animating for the dense laser-scanned face in the low resolution level
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it