  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. Results of
SEELE on ReS demonstrate its efficacy.
