Bibliographic record
Abstract
Abstract We present novel ideas for facial shape and skin simulation on extremely detailed three‐dimensional facial meshes. Our input database is composed of a small number of scanned human faces with resolutions up to several million triangles, where even the pores are clearly distinguished. We show how to decompose the facial meshes into the global shape of the face plus skin detail (3D skin), and then to reconstitute them. Our modeling methodology allows us to simulate the exaggeration of the facial global shape, retaining the original skin detail, as well as to transfer 3D skin from one face to another. First, we represent all the input faces in terms of a homogeneous structure on the base model in low resolution by using mesh adaptation techniques. Second, the differences between the original mesh and a base mesh, which appear as skin detail, are captured and stored, so that each face is decomposed into the global shape (a base mesh) plus skin detail. Face reconstitution after global shape exaggeration and/or skin transfer enables delicate simulation of facial models. In addition, we can increase the resolution of any model scanned at a low resolution by transferring skin from a higher resolution model. Our method shows successful manipulation of the minute structure of 3D skin differently from other methods such as Normal Mapping, Displacement Mapping, Displaced Subdivision Surfaces, and Normal Meshes where none of these techniques show manipulation of minute structure like ours and only approximation is used while our method recovers the original structure. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".