Learning to Search for Executable Patternmaking from Certified Programs

Abstract:Recent models generate sewing patterns from images or text, and program-based representations make these patterns executable. These systems produce a finished pattern and do not model how a patternmaker turns one pattern into another. In practice a new style is made by editing an existing pattern: lengthening the bodice, moving a dart, swapping a sleeve. Pattern archives record the results of such edits and none of the steps. We observe that the steps can be recovered by searching over editing actions until one pattern becomes the other. A deterministic compiler makes the recovered steps checkable. To this end, we present SemDraft, the first framework that learns to search for executable patternmaking programs from paired before-and-after sewing patterns without recorded editing steps. Specifically, a bounded search recovers a program of editing actions and certifies it only after three checks: it recompiles from scratch, every pair of its actions is classified as ordered or interchangeable, and an independent replay reaches the same pattern. The certified programs then train a model that ranks candidate actions inside the same search. On two frozen draws of 80 order-sensitive cases, with every other search component fixed, the learned ranker certifies 63 and 64 cases within 1,000 search executions, against at most 53 and 52 for non-learned rankers.

https://arxiv.org/submit/8234086/start

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Graphics (cs.GR)

arXiv:2610.21579 [cs.LG]

(or arXiv:2610.21579v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.21579 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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