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. For our framework, we first introduce SewingGPT, a GPT-based architecture integrating cross-attention with text-conditioned embedding to generate sewing patterns with text guidance. We also tailored a pre-trained Stable Diffusion for high-quality, tile-based PBR texture generation. By leveraging a large language model, our framework generates CG-friendly garments through natural language interaction. Our method also facilitates pattern completion and texture editing, simplifying the process for designers by user-friendly interaction. With comprehensive evaluations and comparisons with other state-of-the-art methods, our method showcases the best quality and alignment with input prompts. User studies further validate our high-quality rendering results, highlighting its practical utility and potential in production settings.

通过引入基于GPT的体系结构SewingGPT，结合文本条件嵌入和跨注意力，我们的框架可通过自然语言交互生成与计算机生成图形友好的服装。通过与其他最先进方法的综合评估和比较，我们的方法展示了最好的质量和输入提示的一致性。用户研究进一步验证了我们高质量的渲染结果，突显了它在实际生产环境中的实用性和潜力。

DressCode：基于文本引导的自回归缝制与生成服装