Garment pattern design aims to convert a 3D garment to the corresponding 2D panels and their sewing structure. Existing methods rely either on template fitting with heuristics and prior assumptions, or on model learning with complicated shape parameterization. Importantly, both approaches do not allow for personalization of the output garment, which today has increasing demands. To fill this demand, we introduce PersonalTailor: a personalized 2D pattern design method, where the user can input specific constraints or demands (in language or sketch) for personal 2D panel fabrication from 3D point clouds. PersonalTailor first learns a multi-modal panel embeddings based on unsupervised cross-modal association and attentive fusion. It then predicts a binary panel masks individually using a transformer encoder-decoder framework. Extensive experiments show that our PersonalTailor excels on both personalized and standard pattern fabrication tasks.

翻译：服装版型设计旨在将三维服装转化为对应的二维裁片及其缝合结构。现有方法要么依赖基于启发式规则和先验假设的模板拟合，要么采用复杂形状参数化的模型学习。值得注意的是，这两种方法均无法实现如今日益增长的服装个性化输出需求。为填补这一空白，我们提出个性化裁剪（PersonalTailor）：一种个性化二维版型设计方法，用户可通过语言或草图输入特定约束条件，从三维点云生成个性化二维裁片。该方法首先基于无监督跨模态关联与注意力融合学习多模态裁片嵌入，随后采用Transformer编码-解码框架独立预测各裁片的二值掩膜。大量实验表明，个性化裁剪在个性化版型生成与标准版型生成任务中均表现优异。