Woven fabric materials are widely used in rendering applications, yet designing realistic examples typically involves multiple stages, requiring expertise in weaving principles and texture authoring. Recent advances have explored diffusion models to streamline this process; however, pre-trained diffusion models often struggle to generate intricate yarn-level details that conform to weaving rules. To address this, we present FabricGen, an end-to-end framework for generating high-quality woven fabric materials from textual descriptions. A key insight of our method is the decomposition of macro-scale textures and micro-scale weaving patterns. To generate macro-scale textures free from microstructures, we fine-tune pre-trained diffusion models on a collected dataset of microstructure-free fabrics. As for micro-scale weaving patterns, we develop an enhanced procedural geometric model capable of synthesizing natural yarn-level geometry with yarn sliding and flyaway fibers. The procedural model is driven by a specialized large language model, WeavingLLM, which is fine-tuned on an annotated dataset of formatted weaving drafts, and prompt-tuned with domain-specific fabric expertise. Through fine-tuning and prompt tuning, WeavingLLM learns to design weaving drafts and fabric parameters from textual prompts, enabling the procedural model to produce diverse weaving patterns that stick to weaving principles. The generated macro-scale texture, along with the micro-scale geometry, can be used for fabric rendering. Consequently, our framework produces materials with significantly richer detail and realism compared to prior generative models.

翻译：机织面料材料在渲染应用中广泛使用，然而设计逼真的示例通常涉及多个阶段，需要具备编织原理和纹理创作的专业知识。最近的进展探索了使用扩散模型来简化这一过程；然而，预训练的扩散模型往往难以生成符合编织规则的复杂纱线级细节。为了解决这个问题，我们提出了FabricGen，一个从文本描述生成高质量机织面料材料的端到端框架。我们方法的一个关键见解是将宏观尺度纹理与微观尺度编织图案进行解耦。为了生成无微观结构的宏观尺度纹理，我们在一个收集的无微结构面料数据集上对预训练的扩散模型进行了微调。对于微观尺度的编织图案，我们开发了一个增强的程序化几何模型，能够合成具有纱线滑移和飞散纤维的自然纱线级几何结构。该程序化模型由一个专门的大型语言模型WeavingLLM驱动，该模型在一个带标注的格式化编织图数据集上进行了微调，并利用特定领域的织物专业知识进行了提示调优。通过微调和提示调优，WeavingLLM学会了从文本提示中设计编织图和织物参数，从而使程序化模型能够生成遵循编织原则的多样化编织图案。生成的宏观尺度纹理与微观尺度几何结构可用于织物渲染。因此，与先前的生成模型相比，我们的框架生成的材质具有显著更丰富的细节和真实感。