  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/.
