title: DressCode: Autoregressively Sewing and Generating Garments from Text
    Guidance
summary: 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.
paper_authors._id: 660a65e04fa3a72a97bb6f57
paper_authors.name: Kai He
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-30T11:55:19.202Z
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paper_authors.fullname: Kai He
paper_authors.type: user
paper_authors.user: IHe-KaiI
paper_authors._id: 660a65e04fa3a72a97bb6f58
paper_authors.name: Kaixin Yao
paper_authors._id: 660a65e04fa3a72a97bb6f59
paper_authors.name: Qixuan Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-30T08:29:39.463Z
paper_authors._id: 661d10eee7b0ab12bc6721d1
paper_authors.avatarUrl: /avatars/a35c91825baa90d4b0703a1e06ccc5f4.svg
paper_authors.fullname: Qixuan Zhang
paper_authors.type: user
paper_authors.user: Haaribo
paper_authors._id: 660a65e04fa3a72a97bb6f5a
paper_authors.name: Jingyi Yu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-30T08:29:46.657Z
paper_authors._id: 63ffcec6e7767a89533b8f80
paper_authors.avatarUrl: /avatars/b9e8673943741c2e7b80b15a061bb951.svg
paper_authors.fullname: JINGYI Yu
paper_authors.type: user
paper_authors.user: Daluuu
paper_authors._id: 660a65e04fa3a72a97bb6f5b
paper_authors.name: Lingjie Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-30T08:29:56.621Z
paper_authors._id: 65ef493cd08d92badad38e54
paper_authors.avatarUrl: /avatars/734fde502646760eab2c82ea87bfdff0.svg
paper_authors.fullname: liulingie
paper_authors.type: user
paper_authors.user: liulingjie
paper_authors._id: 660a65e04fa3a72a97bb6f5c
paper_authors.name: Lan Xu
paper_title: DressCode: Autoregressively Sewing and Generating Garments from Text
    Guidance
paper_summary: 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.
paper_ai_keywords: GPT-based architecture text-conditioned embedding SewingGPT Stable Diffusion PBR texture generation natural language interaction pattern completion texture editing