3D Facial Model Synthesis using Voronoi Approach
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
Construction and animation of realistic human facial models is an important research field of computer graphics. How to efficiently create an individualized facial model for animation is still a challenge. In this paper, we present a method for 3D facial model synthesis that combines the traditional free-form deformation (FFD) model with techniques of data interpolation based on Dirichlet/Voronoi diagrams. With 18 feature points extracted from 2D facial images in two orthogonal views, Dirichlet free form deformation (DFFD) is utilized for modifying a generic 3D face to produce the individual face. The main advantages of this method over former extensions of FFD is in removing the constraints on control lattice and control points location. In addition, by assigning different weights to those control points, we improve the DFFD algorithm to make it more adaptable to the facial deformation. The reconstructed 3D faces can be used to generate different facial animations.
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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