Apparel's significant role in human appearance underscores the importance of garment digitalization for digital human creation. Recent advances in 3D content creation are pivotal for digital human creation. Nonetheless, garment generation from text guidance is still nascent. We introduce a text-driven 3D garment generation framework, DressCode, which aims to democratize design for novices and offer immense potential in fashion design, virtual try-on, and digital human creation. We first introduce SewingGPT, a GPT-based architecture integrating cross-attention with text-conditioned embedding to generate sewing patterns with text guidance. We then tailor a pre-trained Stable Diffusion to generate tile-based Physically-based Rendering (PBR) textures for the garments. By leveraging a large language model, our framework generates CG-friendly garments through natural language interaction. It also facilitates pattern completion and texture editing, streamlining the design process through user-friendly interaction. This framework fosters innovation by allowing creators to freely experiment with designs and incorporate unique elements into their work. With comprehensive evaluations and comparisons with other state-of-the-art methods, our method showcases superior quality and alignment with input prompts. User studies further validate our high-quality rendering results, highlighting its practical utility and potential in production settings. Our project page is https://IHe-KaiI.github.io/DressCode/.

翻译：服装在人类外观中扮演着重要角色，凸显了服装数字化对于数字人创建的关键意义。三维内容生成的最新进展对数字人创建至关重要。然而，基于文本引导的服装生成仍处于起步阶段。我们提出了一个文本驱动的三维服装生成框架DressCode，旨在为新手普及设计工具，并在时尚设计、虚拟试衣及数字人创建等领域提供巨大潜力。我们首先提出了SewingGPT，这是一种基于GPT的架构，通过将交叉注意力与文本条件嵌入相结合，在文本引导下生成缝制图案。随后，我们微调了预训练的Stable Diffusion模型，以生成基于图块的物理渲染（PBR）服装纹理。通过利用大语言模型，我们的框架能够通过自然语言交互生成适用于计算机图形学的服装。该框架还支持图案补全与纹理编辑，通过用户友好的交互简化设计流程。这一框架鼓励创新，使创作者能够自由尝试设计并将独特元素融入作品。通过全面的评估以及与当前先进方法的比较，我们的方法展现出更优的生成质量和对输入提示的准确对齐。用户研究进一步验证了我们高质量的渲染结果，凸显了其在生产环境中的实用价值与潜力。项目页面为 https://IHe-KaiI.github.io/DressCode/。