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：服装纸样定义了服装的结构基础，对于时装设计、制造和物理模拟等应用至关重要。尽管在自动化纸样生成方面取得了进展，但由于面板几何形状和缝线排列的广泛变异性，精确建模服装纸样仍然困难。在这项工作中，我们提出了一种基于隐式表示的服装纸样建模方法。我们使用定义其边界的符号距离场和标识缝线端点的无符号距离场来表示每个面板，并将这些场编码到连续潜在空间中，从而实现可微网格化。潜流匹配模型学习该表示中面板组合的分布，而缝线预测模块从提取的边片段中恢复缝线关系。该公式允许对具有复杂结构的服装纸样进行精确建模和生成。我们进一步表明，与现有方法相比，它可用于从图像中估计服装纸样且精度更高，并支持纸样补全和重设等应用，为数字时装设计提供了实用工具。