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.

翻译：摘要：服装在人类外观中扮演着重要角色，这使得服装数字化对于数字人类创作至关重要。近期3D内容创作的进展对数字人类制作具有关键意义，但基于文本引导的服装生成技术仍处于起步阶段。我们提出一种文本驱动的3D服装生成框架DressCode，旨在让非专业人士也能轻松进行设计，并在时装设计、虚拟试穿及数字人类创作领域具有巨大潜力。该框架中，我们首先引入SewingGPT——一种基于GPT架构的模型，通过交叉注意力机制与文本条件嵌入相结合，实现文本引导下的缝纫图案生成。同时，我们定制了预训练的Stable Diffusion模型，用于生成基于图块的高质量PBR纹理。通过利用大语言模型，本框架能够通过自然语言交互生成符合计算机图形学标准的服装。我们的方法还支持图案补全与纹理编辑，通过友好的人机交互简化设计师工作流程。经过全面评估并与现有最优方法对比，本方法在生成质量与文本提示一致性方面均达到最佳效果。用户研究进一步验证了其高质量渲染结果，凸显了该方法的实用价值及在工业化生产中的潜力。