3D-Layers: Bringing Layer-Based Color Editing to VR Painting
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
The ability to represent artworks as stacks of layers is fundamental to modern graphics design, as it allows artists to easily separate visual elements, edit them in isolation, and blend them to achieve rich visual effects. Despite their ubiquity in 2D painting software, layers have not yet made their way to VR painting, where users paint strokes directly in 3D space by gesturing a 6-degrees-of-freedom controller. But while the concept of a stack of 2D layers was inspired by real-world layers in cell animation, what should 3D layers be? We propose to define 3D-Layers as groups of 3D strokes, and we distinguish the ones that represent 3D geometry from the ones that represent color modifications of the geometry. We call the former substrate layers and the latter appearance layers. Strokes in appearance layers modify the color of the substrate strokes they intersect. Thanks to this distinction, artists can define sequences of color modifications as stacks of appearance layers, and edit each layer independently to finely control the final color of the substrate. We have integrated 3D-Layers into a VR painting application and we evaluate its flexibility and expressiveness by conducting a usability study with experienced VR artists.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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