Abstract
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.
Abstract (translated)
服装 patterns design 的目标是将三维服装转化为对应的二维Panel及其缝合结构。现有的方法要么依赖于模板的启发式fitting和先前假设,要么依赖于复杂的形状参数化模型学习。重要的是,这两种方法都不允许对输出服装进行个性化,而这种需求今天越来越受欢迎。为了满足这种需求,我们介绍了 PersonalTailor:一种个性化的二维pattern设计方法,用户可以从3D点云中输入具体的约束或要求(用语言或草图表示)进行个性化二维Panel制造。 PersonalTailor 首先基于无监督的跨模态结合和注意融合学习多模态Panel嵌入。然后使用transformer编码器-解码框架单独预测二进制Panel口罩。广泛的实验表明,我们的 PersonalTailor 在个性化和标准pattern制造任务中表现优异。
URL
https://arxiv.org/abs/2303.09695
