Voronoi Tiling, Digital Fabric Printing and Interactive eGarments - Designing the iBody
Bibliographic record
Abstract
Traditionally, fabric prints were designed using a regular tiling pattern in a design step completely separate from garment pattern design and garment construction. With the emergence and ongoing spread of digital fabric printers, these different design steps may now be combined so that fabric prints can be designed as a function of how they will appear on the final garment, and then the stages of construction decomposed to obtain the desired result. For repetitive prints, however, whether of a regular or irregular nature, there is a need to be able to dynamically modify the organisation of the tiling to ensure the most effective tile placement in the end result. In addition, this tiling must ideally be sensitive to the draping properties of the human body as well as the location of various tiles with respect to different body parts, even in a static printing solution. The use of dynamically modifiable tiling structures can facilitate this process. We explore the adaptation and use of Voronoi tilings to this problem, including the use of digital surface draping of dynamic Voronoi tiles onto a human body simulcrum. Furthermore, through the use of Augmented Reality, we propose to exploit the power of the dynamic Voronoi diagram to create changing print patterns that adjust to the movement of the body.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".