FashionComposer: Compositional Fashion Image Generation
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.
6763939d3515f1ec56b43dcd
Sihui Ji
claimed_verified
2025-06-05T07:30:36.747Z
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Sihui Ji
user
zjuJish
6763939d3515f1ec56b43dce
Yiyang Wang
admin_assigned
2024-12-20T09:47:55.708Z
66f2c5cb74ca9462f8454311
/avatars/42c7614660238310ef65867736ba27fa.svg
Chandler
user
Chandler14
6763939d3515f1ec56b43dcf
Xi Chen
claimed_verified
2024-12-19T18:01:17.492Z
644a1b6401e18bf93a6f45c1
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xichen
user
xichenhku
6763939d3515f1ec56b43dd0
Xiaogang Xu
admin_assigned
2024-12-20T09:47:42.271Z
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/avatars/11b7d29b6fa6c1b392641e0cd4002863.svg
Xiaogang Xu
user
xiaogang00
6763939d3515f1ec56b43dd1
Hao Luo
6763939d3515f1ec56b43dd2
Hengshuang Zhao
FashionComposer: Compositional Fashion Image Generation
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.
FashionComposer generates personalized fashion images using multi-modal input, featuring a universal framework, asset library, reference UNet, and subject-binding attention.
multi-modal input text prompt parametric human model garment image face image universal framework scaled training data asset library reference UNet subject-binding attention semantic features human album generation virtual try-on tasks