GarmentWeaver能从草图和文本生成可执行缝纫模式，比现有方法更准确且兼容性好。
GarmentWeaver是一种新型框架，通过激活服装相关结构分支构建紧凑层次化目标，从草图和文本描述中推断可执行缝纫模式。该模型基于预训练视觉语言模型构建，引入了感知可行性的正则化方法。实验表明，GarmentWeaver比基线方法生成更准确、更可执行的缝纫模式，同时获得更好的模拟结果。
GarmentWeaver: Schema-Aware Structured Synthesis for Multimodal Sewing Patterns
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.