Repositioning the Subject within Image
Current image manipulation primarily centers on static manipulation, such as
replacing specific regions within an image or altering its overall style. In
this paper, we introduce an innovative dynamic manipulation task, subject
repositioning. This task involves relocating a user-specified subject to a
desired position while preserving the image's fidelity. Our research reveals
that the fundamental sub-tasks of subject repositioning, which include filling
the void left by the repositioned subject, reconstructing obscured portions of
the subject and blending the subject to be consistent with surrounding areas,
can be effectively reformulated as a unified, prompt-guided inpainting task.
Consequently, we can employ a single diffusion generative model to address
these sub-tasks using various task prompts learned through our proposed task
inversion technique. Additionally, we integrate pre-processing and
post-processing techniques to further enhance the quality of subject
repositioning. These elements together form our SEgment-gEnerate-and-bLEnd
(SEELE) framework. To assess SEELE's effectiveness in subject repositioning, we
assemble a real-world subject repositioning dataset called ReS. Our results on
ReS demonstrate the quality of repositioned image generation.
65b9c031efa633aa9a8f273d
Yikai Wang
admin_assigned
2024-01-31T09:56:55.169Z
63463bc4547c70e4b7d3009f
/avatars/6e5350fd998f0a7a4143d7504218164a.svg
Yikai Wang
user
yikaiwang
65b9c031efa633aa9a8f273e
Chenjie Cao
admin_assigned
2024-01-31T09:57:06.042Z
64892bc82eafb7b91182bec5
/avatars/c9eed91e60f3ea227b35e111d3fc4200.svg
chenjie cao
user
ewrfcas
65b9c031efa633aa9a8f273f
Qiaole Dong
admin_assigned
2024-01-31T09:57:19.143Z
656daf49edd446c42b9490b0
/avatars/8cd878f33cff824973c93283ff232861.svg
Qiaole Dong
user
DQiaole
65b9c031efa633aa9a8f2740
Yifan Li
65b9c031efa633aa9a8f2741
Yanwei Fu
Repositioning the Subject within Image
Current image manipulation primarily centers on static manipulation, such as
replacing specific regions within an image or altering its overall style. In
this paper, we introduce an innovative dynamic manipulation task, subject
repositioning. This task involves relocating a user-specified subject to a
desired position while preserving the image's fidelity. Our research reveals
that the fundamental sub-tasks of subject repositioning, which include filling
the void left by the repositioned subject, reconstructing obscured portions of
the subject and blending the subject to be consistent with surrounding areas,
can be effectively reformulated as a unified, prompt-guided inpainting task.
Consequently, we can employ a single diffusion generative model to address
these sub-tasks using various task prompts learned through our proposed task
inversion technique. Additionally, we integrate pre-processing and
post-processing techniques to further enhance the quality of subject
repositioning. These elements together form our SEgment-gEnerate-and-bLEnd
(SEELE) framework. To assess SEELE's effectiveness in subject repositioning, we
assemble a real-world subject repositioning dataset called ReS. Our results on
ReS demonstrate the quality of repositioned image generation.
A diffusion generative model, using SEELE framework, handles dynamic subject repositioning by reformulating the task into unified prompt-guided inpainting with task inversion techniques.
diffusion generative model subject repositioning prompt-guided inpainting task inversion SEELE framework