GarmageNet: A Multimodal Generative Framework for Sewing Pattern Design and Generic Garment Modeling

• URL: http://arxiv.org/abs/2504.01483v3
• Date: Mon, 09 Jun 2025 11:06:19 GMT
• Title: GarmageNet: A Multimodal Generative Framework for Sewing Pattern Design and Generic Garment Modeling
• Authors: Siran Li, Chen Liu, Ruiyang Liu, Zhendong Wang, Gaofeng He, Yong-Lu Li, Xiaogang Jin, Huamin Wang,
• Abstract summary: GarmageNet is a generative framework that automates the creation of 2D sewing patterns.<n>Garmage is a novel garment representation that encodes each panel as a structured geometry image.<n>GarmageSet is a large-scale dataset comprising over 10,000 professionally designed garments.
• Score: 31.086617193645022
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 shapes. GarmageNet employs a latent diffusion transformer to synthesize panel-wise geometry images and integrates 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 over 10,000 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.

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