Sewing patterns define the structural foundation of garments and are essential for applications such as fashion design, fabrication, and physical simulation. Despite progress in automated pattern generation, accurately modeling sewing patterns remains difficult due to the broad variability in panel geometry and seam arrangements. In this work, we introduce a sewing pattern modeling method based on an implicit representation. We represent each panel using a signed distance field that defines its boundary and an unsigned distance field that identifies seam endpoints, and encode these fields into a continuous latent space that enables differentiable meshing. A latent flow matching model learns distributions over panel combinations in this representation, and a stitching prediction module recovers seam relations from extracted edge segments. This formulation allows accurate modeling and generation of sewing patterns with complex structures. We further show that it can be used to estimate sewing patterns from images with improved accuracy relative to existing approaches, and supports applications such as pattern completion and refitting, providing a practical tool for digital fashion design.

翻译：缝纫纸样定义了服装的结构基础，对于时装设计、制作及物理仿真等应用至关重要。尽管自动纸样生成技术已取得进展，但由于裁片几何形状与接缝排布的广泛多样性，精确建模缝纫纸样仍然困难。本研究提出一种基于隐式表示的缝纫纸样建模方法。我们使用有符号距离场定义每个裁片的边界，并通过无符号距离场标识接缝端点，将这些场编码至一个连续的潜在空间，从而实现可微分网格化。一个潜在流匹配模型在该表示中学习裁片组合的分布，同时通过接缝预测模块从提取的边缘段中恢复接缝关系。此框架能够精确建模并生成具有复杂结构的缝纫纸样。我们进一步证明，相较于现有方法，该框架能够以更高精度从图像中估计缝纫纸样，并支持纸样补全与尺寸调整等应用，为数字化时装设计提供了实用工具。