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
Creating realistic-looking skin is one of the holy grails of computer graphics and is still an active area of research. The problem is challenging due to the inherent complexity of skin and its variations, not only across individuals but also spatially and temporally among one. Skin appearance and reflectance vary spatially in one individual depending on its location on the human body, but also vary temporally with the aging process and the body state. Emotions, health, physical activity, and cosmetics for example can all affect the appearance of skin. The spatially varying reflectance of skin is due to many parameters, such as skin micro- and meso-geometry, thickness, oiliness, and pigmentation. It is therefore a daunting task to derive a model that will include all these parameters to produce realistic-looking skin. The problem is also compounded by the fact that we are very well accustomed to the appearance of skin and especially sensitive to facial appearances and expressions. Skin modelling and rendering is crucial for many applications such as games, virtual reality, films, and the beauty industry, to name a few. Realistic-looking skin improves the believability and realism of applications. The complexity of skin makes the topic of skin modelling and rendering for computer graphics a very difficult, but highly stimulating one. Skin deformations and biomechanics is a vast topic that we will not address in this dissertation. We rather focus our attention on skin optics and present a simple model for the reflectance of human skin along with a system to support skin modelling and rendering.
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 | 0.001 | 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