发表机构
Donghua University; Ningbo University（东华大学; 宁波大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
GarmentWeaver是一种模式感知多模态缝纫图案生成框架，通过分层结构化建模与可行性正则化，生成更准确、可执行且仿真兼容的缝纫图案，性能优于基线方法。
AI 中文摘要
多模态缝纫图案生成旨在从草图、文本描述等设计线索中推断出可执行的缝纫图案，作为一种可解释且可与仿真兼容的表示形式，缝纫图案对数字服装创作尤为重要。然而，现有方法通常将服装规格建模为扁平长序列，这会混淆服装结构与详细参数，导致组件冗余、局部细节不准确、仿真兼容性差。本文提出GarmentWeaver，一种面向多模态缝纫图案生成的模式感知框架。GarmentWeaver通过激活与服装相关的结构分支来构建紧凑的分层目标，并以结构化方式预测可执行的缝纫图案。具体而言，我们引入模式感知目标构建策略，基于预训练的视觉-语言模型构建生成器以实现多模态服装理解，并施加可行性感知正则化以鼓励输出在结构上有效且与仿真兼容。大量实验表明，与强基线相比，GarmentWeaver生成的缝纫图案更准确、更具可执行性，同时仿真结果也更好。这些发现证明了模式感知结构化生成在可靠多模态缝纫图案预测中的有效性。
英文摘要
Multimodal Sewing pattern generation aims to infer executable sewing patterns from design cues such as sketches and textual descriptions. As an interpretable and simulation-compatible representation, sewing patterns are particularly valuable for digital garment creation. However, existing methods often model garment specifications as flat long sequences, which entangles garment structure with detailed parameters and leads to redundant components, inaccurate local details, and poor simulation compatibility. In this paper, we present GarmentWeaver, a schema-aware framework for multimodal Sewing pattern generation. GarmentWeaver constructs compact hierarchical targets by activating garment-relevant structural branches and predicts executable Sewing patterns in a structured manner. Specifically, we introduce a schema-aware target construction strategy, build the generator on top of a pretrained vision-language model for multimodal garment understanding, and impose feasibility-aware regularization to encourage structurally valid and simulation-compatible outputs. Extensive experiments show that GarmentWeaver produces more accurate and more executable sewing patterns than strong baselines, while also yielding better simulation results. These findings demonstrate the effectiveness of schema-aware structured generation for reliable multimodal Sewing pattern prediction.
Comments10 pages, 7 figures
