Virtual clothes, hair and skin for beautiful top models
Bibliographic record
Abstract
Since 1986, we have led extensive research on simulating realistic looking humans. We have created Marilyn Monroe and Humphrey Bogart that met in a Cafe in Montreal. At that time, they did not wear any dress as such. Humphrey's body was made out of a plaster model that has the shape of a suit. Colours on Marilyn's body looked like a dress. Hairs were simulated as a global shape and skin was a colour. Since then, we have developed extensive research to simulate real virtual deformable clothes wearing by virtual humans. We also needed to have appropriate simulation of a skin and recently, we have developed new research on skin in order to decrease the plastic colour of our synthetic actors. Also there was a need to simulate hair in an efficient way. New methods have been developed, both for design and animation purpose that are compatible with the clothes module. In this paper, we introduce our most recent research results on these topics. We are now able to simulate top models that start to look like real ones. Our latest work, that shows Marilyn receiving a golden camera Award in Berlin, Germany, demonstrates the results of our research. This sequence has been shown in a ZDF television program that was seen by more than 15 million viewers.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".