MétaCan
Menu
Back to cohort
Record W2803026146 · doi:10.1145/3197517.3201310

Foldsketch

2018· article· en· W2803026146 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACM Transactions on Graphics · 2018
Typearticle
Languageen
FieldEngineering
Topic3D Shape Modeling and Analysis
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsSchematicComputer scienceGeometrySimple (philosophy)Computer graphics (images)Fold (higher-order function)SoftwareGeometric modelingEngineering drawingMathematicsEngineeringProgramming language

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.925
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.233
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it