DressCode: Autoregressively Sewing and Generating Garments from Text
  Guidance
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.
660a65e04fa3a72a97bb6f57
Kai He
claimed_verified
2024-04-30T11:55:19.202Z
6630cb71b0a85e18019d4969
/avatars/ee76ab93e9566c0e5f6b699dcc10548c.svg
Kai He
user
IHe-KaiI
660a65e04fa3a72a97bb6f58
Kaixin Yao
660a65e04fa3a72a97bb6f59
Qixuan Zhang
admin_assigned
2024-04-30T08:29:39.463Z
661d10eee7b0ab12bc6721d1
/avatars/a35c91825baa90d4b0703a1e06ccc5f4.svg
Qixuan Zhang
user
Haaribo
660a65e04fa3a72a97bb6f5a
Jingyi Yu
admin_assigned
2024-04-30T08:29:46.657Z
63ffcec6e7767a89533b8f80
/avatars/b9e8673943741c2e7b80b15a061bb951.svg
JINGYI Yu
user
Daluuu
660a65e04fa3a72a97bb6f5b
Lingjie Liu
admin_assigned
2024-04-30T08:29:56.621Z
65ef493cd08d92badad38e54
/avatars/734fde502646760eab2c82ea87bfdff0.svg
liulingie
user
liulingjie
660a65e04fa3a72a97bb6f5c
Lan Xu
DressCode: Autoregressively Sewing and Generating Garments from Text
  Guidance
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.
A text-driven 3D garment generation framework, DressCode, uses SewingGPT and Stable Diffusion to create high-quality, CG-friendly garments through natural language interaction.
GPT-based architecture cross-attention text-conditioned embedding SewingGPT pre-trained Stable Diffusion tile-based PBR texture generation pattern completion texture editing