title: Repositioning the Subject within Image
summary: 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.
paper_authors._id: 65b9c031efa633aa9a8f273d
paper_authors.name: Yikai Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-31T09:56:55.169Z
paper_authors._id: 63463bc4547c70e4b7d3009f
paper_authors.avatarUrl: /avatars/6e5350fd998f0a7a4143d7504218164a.svg
paper_authors.fullname: Yikai Wang
paper_authors.type: user
paper_authors.user: yikaiwang
paper_authors._id: 65b9c031efa633aa9a8f273e
paper_authors.name: Chenjie Cao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-31T09:57:06.042Z
paper_authors._id: 64892bc82eafb7b91182bec5
paper_authors.avatarUrl: /avatars/c9eed91e60f3ea227b35e111d3fc4200.svg
paper_authors.fullname: chenjie cao
paper_authors.type: user
paper_authors.user: ewrfcas
paper_authors._id: 65b9c031efa633aa9a8f273f
paper_authors.name: Qiaole Dong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-31T09:57:19.143Z
paper_authors._id: 656daf49edd446c42b9490b0
paper_authors.avatarUrl: /avatars/8cd878f33cff824973c93283ff232861.svg
paper_authors.fullname: Qiaole Dong
paper_authors.type: user
paper_authors.user: DQiaole
paper_authors._id: 65b9c031efa633aa9a8f2740
paper_authors.name: Yifan Li
paper_authors._id: 65b9c031efa633aa9a8f2741
paper_authors.name: Yanwei Fu
paper_title: Repositioning the Subject within Image
paper_summary: 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.
paper_ai_summary: A diffusion generative model, using SEELE framework, handles dynamic subject repositioning by reformulating the task into unified prompt-guided inpainting with task inversion techniques.
paper_ai_keywords: diffusion generative model subject repositioning prompt-guided inpainting task inversion SEELE framework