Realistic digital garment modeling remains a labor-intensive task due to the intricate process of translating 2D sewing patterns into high-fidelity, simulation-ready 3D garments. We introduce GarmageNet, a unified generative framework that automates the creation of 2D sewing patterns, the construction of sewing relationships, and the synthesis of 3D garment initializations compatible with physics-based simulation. Central to our approach is Garmage, a novel garment representation that encodes each panel as a structured geometry image, effectively bridging the semantic and geometric gap between 2D structural patterns and 3D garment geometries. Followed by GarmageNet, a latent diffusion transformer to synthesize panel-wise geometry images and GarmageJigsaw, a neural module for predicting point-to-point sewing connections along panel contours. To support training and evaluation, we build GarmageSet, a large-scale dataset comprising 14,801 professionally designed garments with detailed structural and style annotations. Our method demonstrates versatility and efficacy across multiple application scenarios, including scalable garment generation from multi-modal design concepts (text prompts, sketches, photographs), automatic modeling from raw flat sewing patterns, pattern recovery from unstructured point clouds, and progressive garment editing using conventional instructions, laying the foundation for fully automated, production-ready pipelines in digital fashion. Project page: https://style3d.github.io/garmagenet/.

翻译：逼真的数字服装建模仍然是一项劳动密集型任务，这源于将二维缝纫图案转化为高保真、可用于仿真的三维服装的复杂过程。我们提出了GarmageNet，一个统一的生成框架，能够自动创建二维缝纫图案、构建缝纫关系，并合成与基于物理的仿真兼容的三维服装初始化模型。我们方法的核心是Garmage，这是一种新颖的服装表示方法，它将每个裁片编码为结构化的几何图像，有效弥合了二维结构图案与三维服装几何之间的语义和几何鸿沟。基于此，我们构建了GarmageNet，一个用于合成逐裁片几何图像的潜在扩散Transformer模型，以及GarmageJigsaw，一个用于预测沿裁片轮廓的点对点缝纫连接的神经模块。为了支持训练和评估，我们构建了GarmageSet，这是一个大规模数据集，包含14,801件专业设计的服装，并附有详细的结构和风格标注。我们的方法在多种应用场景中展现了其多功能性和高效性，包括从多模态设计概念（文本提示、草图、照片）进行可扩展的服装生成、从原始平面缝纫图案自动建模、从非结构化点云恢复图案，以及使用常规指令进行渐进式服装编辑，为数字时尚领域全自动、可用于生产的流程奠定了基础。项目页面：https://style3d.github.io/garmagenet/。