title: Click2Mask: Local Editing with Dynamic Mask Generation
summary: Recent advancements in generative models have revolutionized image generation
  and editing, making these tasks accessible to non-experts. This paper focuses
  on local image editing, particularly the task of adding new content to a
  loosely specified area. Existing methods often require a precise mask or a
  detailed description of the location, which can be cumbersome and prone to
  errors. We propose Click2Mask, a novel approach that simplifies the local
  editing process by requiring only a single point of reference (in addition to
  the content description). A mask is dynamically grown around this point during
  a Blended Latent Diffusion (BLD) process, guided by a masked CLIP-based
  semantic loss. Click2Mask surpasses the limitations of segmentation-based and
  fine-tuning dependent methods, offering a more user-friendly and contextually
  accurate solution. Our experiments demonstrate that Click2Mask not only
  minimizes user effort but also delivers competitive or superior local image
  manipulation results compared to SoTA methods, according to both human
  judgement and automatic metrics. Key contributions include the simplification
  of user input, the ability to freely add objects unconstrained by existing
  segments, and the integration potential of our dynamic mask approach within
  other editing methods.
paper_authors._id: 66e83376a3f5a4cad5ebde9b
paper_authors.name: Omer Regev
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-17T07:57:06.688Z
paper_authors._id: 632add454f9006565ae542be
paper_authors.avatarUrl: /avatars/0937fd1690052f854a5fb6f80cfca696.svg
paper_authors.fullname: Omer Regev
paper_authors.type: user
paper_authors.user: omeregev
paper_authors._id: 66e83376a3f5a4cad5ebde9c
paper_authors.name: Omri Avrahami
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-17T07:12:03.303Z
paper_authors._id: 62f6ab4fc3372328414c8689
paper_authors.avatarUrl: /avatars/4f728a5b70c9fe4a64e80e2b643ca620.svg
paper_authors.fullname: Omri Avrahami
paper_authors.type: user
paper_authors.user: omriav
paper_authors._id: 66e83376a3f5a4cad5ebde9d
paper_authors.name: Dani Lischinski
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-17T07:57:11.953Z
paper_authors._id: 644cc8a81e0cef1987125d22
paper_authors.avatarUrl: /avatars/a728cefda3b95c39a3c9aa6485a11c60.svg
paper_authors.fullname: Dani Lischinski
paper_authors.type: user
paper_authors.user: danix3d
paper_title: Click2Mask: Local Editing with Dynamic Mask Generation
paper_summary: Recent advancements in generative models have revolutionized image generation
  and editing, making these tasks accessible to non-experts. This paper focuses
  on local image editing, particularly the task of adding new content to a
  loosely specified area. Existing methods often require a precise mask or a
  detailed description of the location, which can be cumbersome and prone to
  errors. We propose Click2Mask, a novel approach that simplifies the local
  editing process by requiring only a single point of reference (in addition to
  the content description). A mask is dynamically grown around this point during
  a Blended Latent Diffusion (BLD) process, guided by a masked CLIP-based
  semantic loss. Click2Mask surpasses the limitations of segmentation-based and
  fine-tuning dependent methods, offering a more user-friendly and contextually
  accurate solution. Our experiments demonstrate that Click2Mask not only
  minimizes user effort but also delivers competitive or superior local image
  manipulation results compared to SoTA methods, according to both human
  judgement and automatic metrics. Key contributions include the simplification
  of user input, the ability to freely add objects unconstrained by existing
  segments, and the integration potential of our dynamic mask approach within
  other editing methods.
paper_ai_keywords: Blended Latent Diffusion (BLD) CLIP-based semantic loss segmentation-based methods fine-tuning dependent methods dynamic mask approach