arXiv cs.AI· Zhuotao Xu, Chi Zeng, Liying Yang, Xinxing Yu, Yanyan Liang·· 2 天前AI 评分19
SemDraft：从成对服装样板中学习搜索可执行制版程序
Learning to Search for Executable Patternmaking from Certified Programs
AI 导读
SemDraft 是首个从成对前后服装样板中学习搜索可执行制版程序的框架，无需记录编辑步骤。它通过有界搜索恢复编辑动作程序，并需通过从头重编译、动作对有序或可互换分类、独立重放三重校验才获认证。在两组各 80 个顺序敏感案例上，学习排序器在 1000 次搜索内分别认证 63 和 64 个案例，非学习排序器最多为 53 和 52 个。
正文 · 原文
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.
| Comments: | https://arxiv.org/submit/8234086/start |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Graphics (cs.GR) |
| Cite as: | arXiv:2610.21579 [cs.LG] |
| (or arXiv:2610.21579v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.21579 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhuotao Xu [view email]
[v1]
Thu, 15 Oct 2026 09:28:18 UTC (231 KB)
来源：arXiv cs.AI · arxiv.org