title: FashionComposer: Compositional Fashion Image Generation
summary: We present FashionComposer for compositional fashion image generation. Unlike
  previous methods, FashionComposer is highly flexible. It takes multi-modal
  input (i.e., text prompt, parametric human model, garment image, and face
  image) and supports personalizing the appearance, pose, and figure of the human
  and assigning multiple garments in one pass. To achieve this, we first develop
  a universal framework capable of handling diverse input modalities. We
  construct scaled training data to enhance the model's robust compositional
  capabilities. To accommodate multiple reference images (garments and faces)
  seamlessly, we organize these references in a single image as an "asset
  library" and employ a reference UNet to extract appearance features. To inject
  the appearance features into the correct pixels in the generated result, we
  propose subject-binding attention. It binds the appearance features from
  different "assets" with the corresponding text features. In this way, the model
  could understand each asset according to their semantics, supporting arbitrary
  numbers and types of reference images. As a comprehensive solution,
  FashionComposer also supports many other applications like human album
  generation, diverse virtual try-on tasks, etc.
paper_authors._id: 6763939d3515f1ec56b43dcd
paper_authors.name: Sihui Ji
paper_authors._id: 6763939d3515f1ec56b43dce
paper_authors.name: Yiyang Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-20T09:47:55.708Z
paper_authors._id: 66f2c5cb74ca9462f8454311
paper_authors.avatarUrl: /avatars/42c7614660238310ef65867736ba27fa.svg
paper_authors.fullname: Yiyang  Wang
paper_authors.type: user
paper_authors.user: Chandler14
paper_authors._id: 6763939d3515f1ec56b43dcf
paper_authors.name: Xi Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-19T18:01:17.492Z
paper_authors._id: 644a1b6401e18bf93a6f45c1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/644a1b6401e18bf93a6f45c1/P0i_CgCrIzOS2tYRlxoE9.png
paper_authors.fullname: xichen
paper_authors.type: user
paper_authors.user: xichenhku
paper_authors._id: 6763939d3515f1ec56b43dd0
paper_authors.name: Xiaogang Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-20T09:47:42.271Z
paper_authors._id: 637ee45b2438d7485b8d8f6a
paper_authors.avatarUrl: /avatars/11b7d29b6fa6c1b392641e0cd4002863.svg
paper_authors.fullname: Xiaogang Xu
paper_authors.type: user
paper_authors.user: xiaogang00
paper_authors._id: 6763939d3515f1ec56b43dd1
paper_authors.name: Hao Luo
paper_authors._id: 6763939d3515f1ec56b43dd2
paper_authors.name: Hengshuang Zhao
paper_title: FashionComposer: Compositional Fashion Image Generation
paper_summary: We present FashionComposer for compositional fashion image generation. Unlike
  previous methods, FashionComposer is highly flexible. It takes multi-modal
  input (i.e., text prompt, parametric human model, garment image, and face
  image) and supports personalizing the appearance, pose, and figure of the human
  and assigning multiple garments in one pass. To achieve this, we first develop
  a universal framework capable of handling diverse input modalities. We
  construct scaled training data to enhance the model's robust compositional
  capabilities. To accommodate multiple reference images (garments and faces)
  seamlessly, we organize these references in a single image as an "asset
  library" and employ a reference UNet to extract appearance features. To inject
  the appearance features into the correct pixels in the generated result, we
  propose subject-binding attention. It binds the appearance features from
  different "assets" with the corresponding text features. In this way, the model
  could understand each asset according to their semantics, supporting arbitrary
  numbers and types of reference images. As a comprehensive solution,
  FashionComposer also supports many other applications like human album
  generation, diverse virtual try-on tasks, etc.
paper_ai_keywords: compositional fashion image generations text-to-fashion Composer multi-modal inputs parametric human model garment image faces image Universal framework robust compositional capabilities input modalities asset library appearance features preference reference UNet subject-binding attention semantics human album generation virtual try-on tasks