Bibliographic record
Abstract
While folds and pleats add interest to garments and cloth objects, incorporating them into an existing design manually or using existing software requires expertise and time. We present FoldSketch , a new system that supports simple and intuitive fold and pleat design. FoldSketch users specify the fold or pleat configuration they seek using a simple schematic sketching interface; the system then algorithmically generates both the fold-enhanced 3D garment geometry that conforms to user specifications, and the corresponding 2D patterns that reproduce this geometry within a simulation engine. While previous work aspired to compute the desired patterns for a given target 3D garment geometry, our main algorithmic challenge is that we do not have target geometry to start with. Real-life garment folds have complex profile shapes, and their exact geometry and location on a garment are intricately linked to a range of physical factors such as fabric properties and the garment's interaction with the wearer's body; it is therefore virtually impossible to predict the 3D shape of a fold-enhanced garment using purely geometric means. At the same time, using physical simulation to model folds requires appropriate 2D patterns and initial drape, neither of which can be easily provided by the user. We obtain both the 3D fold-enhanced garment and its corresponding patterns and initial drape via an alternating 2D-3D algorithm. We first expand the input patterns by allocating excess material for the expected fold formation; we then use these patterns to produce an estimated fold-enhanced drape geometry that balances designer expectations against physical reproducibility. We use the patterns and the estimated drape as input to a simulation generating an initial reproducible output. We improve the output's alignment with designer expectations by progressively refining the patterns and the estimated drape, converging to a final fully physically reproducible fold-enhanced garment. Our experiments confirm that FoldSketch reliably converges to a desired garment geometry and corresponding patterns and drape, and works well with different physical simulators. We demonstrate the versatility of our approach by showcasing a collection of garments augmented with diverse fold and pleat layouts specified via the FoldSketch interface, and further validate our approach via comparisons to alternative solutions and feedback from potential users.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.151 | 0.040 |
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".