SemDraft：从成对服装版型中学习搜索可执行的打版编辑程序
Learning to Search for Executable Patternmaking from Certified Programs
服装打版有个老问题：档案里只有改前改后的版型，没有改法。这篇论文让模型自己搜出编辑步骤还逐一验证，能认证的用例比非学习方法多了十来个。
arXiv 论文提出 SemDraft，一个从前后成对服装版型中恢复打版编辑步骤的框架。它通过有界搜索找出编辑动作程序，并要求通过三项认证：从零重新编译、判定动作间顺序或可互换、独立重放得到相同版型。认证后的程序用于训练模型对候选动作排序。在两次各 80 个顺序敏感用例的测试中，学习型排序器在 1,000 次搜索执行内分别认证 63 和 64 个用例，非学习型排序器最多只认证 53 和 52 个。
Learning to Search for Executable Patternmaking from Certified Programs
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.
