CustomNet: Zero-shot Object Customization with Variable-Viewpoints in
  Text-to-Image Diffusion Models
Incorporating a customized object into image generation presents an
attractive feature in text-to-image generation. However, existing
optimization-based and encoder-based methods are hindered by drawbacks such as
time-consuming optimization, insufficient identity preservation, and a
prevalent copy-pasting effect. To overcome these limitations, we introduce
CustomNet, a novel object customization approach that explicitly incorporates
3D novel view synthesis capabilities into the object customization process.
This integration facilitates the adjustment of spatial position relationships
and viewpoints, yielding diverse outputs while effectively preserving object
identity. Moreover, we introduce delicate designs to enable location control
and flexible background control through textual descriptions or specific
user-defined images, overcoming the limitations of existing 3D novel view
synthesis methods. We further leverage a dataset construction pipeline that can
better handle real-world objects and complex backgrounds. Equipped with these
designs, our method facilitates zero-shot object customization without
test-time optimization, offering simultaneous control over the viewpoints,
location, and background. As a result, our CustomNet ensures enhanced identity
preservation and generates diverse, harmonious outputs.
654093ad992b1c560ed5184a
Ziyang Yuan
654093ad992b1c560ed5184b
Mingdeng Cao
admin_assigned
2023-10-31T11:03:27.750Z
6374a02d0856ac905bfc6113
/avatars/2cbe75c9cc818a647ca6e416f129c96f.svg
Mingdeng Cao
user
Ljzycmd
654093ad992b1c560ed5184c
Xintao Wang
admin_assigned
2023-10-31T11:03:52.814Z
60e272ca6c78a8c122b12127
https://cdn-avatars.huggingface.co/v1/production/uploads/60e272ca6c78a8c122b12127/xldEGBzGrU-bX6IwAw0Ie.jpeg
Xintao Wang
user
Xintao
654093ad992b1c560ed5184d
Zhongang Qi
claimed_verified
2024-11-19T08:40:51.534Z
660cc64f1b14f691990c0ea0
/avatars/f172d3120b22d745a41cc3f2eb499ce6.svg
Zhongang Qi
user
phoenixqza
654093ad992b1c560ed5184e
Chun Yuan
654093ad992b1c560ed5184f
Ying Shan
CustomNet: Zero-shot Object Customization with Variable-Viewpoints in
  Text-to-Image Diffusion Models
Incorporating a customized object into image generation presents an
attractive feature in text-to-image generation. However, existing
optimization-based and encoder-based methods are hindered by drawbacks such as
time-consuming optimization, insufficient identity preservation, and a
prevalent copy-pasting effect. To overcome these limitations, we introduce
CustomNet, a novel object customization approach that explicitly incorporates
3D novel view synthesis capabilities into the object customization process.
This integration facilitates the adjustment of spatial position relationships
and viewpoints, yielding diverse outputs while effectively preserving object
identity. Moreover, we introduce delicate designs to enable location control
and flexible background control through textual descriptions or specific
user-defined images, overcoming the limitations of existing 3D novel view
synthesis methods. We further leverage a dataset construction pipeline that can
better handle real-world objects and complex backgrounds. Equipped with these
designs, our method facilitates zero-shot object customization without
test-time optimization, offering simultaneous control over the viewpoints,
location, and background. As a result, our CustomNet ensures enhanced identity
preservation and generates diverse, harmonious outputs.
CustomNet enhances text-to-image generation by integrating 3D novel view synthesis to customize object viewpoints, locations, and backgrounds, ensuring identity preservation and diverse outputs without test-time optimization.
text-to-image generation 3D novel view synthesis object customization viewpoint location background identity preservation dataset construction pipeline zero-shot customization