DressCode: Autoregressively Sewing and Generating Garments from Text
  Guidance
Kai He Kaixin Yao Qixuan Zhang Jingyi Yu Lingjie Liu Lan Xu
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.