title: ObjectMate: A Recurrence Prior for Object Insertion and Subject-Driven
    Generation
summary: This paper introduces a tuning-free method for both object insertion and
  subject-driven generation. The task involves composing an object, given
  multiple views, into a scene specified by either an image or text. Existing
  methods struggle to fully meet the task's challenging objectives: (i)
  seamlessly composing the object into the scene with photorealistic pose and
  lighting, and (ii) preserving the object's identity. We hypothesize that
  achieving these goals requires large scale supervision, but manually collecting
  sufficient data is simply too expensive. The key observation in this paper is
  that many mass-produced objects recur across multiple images of large unlabeled
  datasets, in different scenes, poses, and lighting conditions. We use this
  observation to create massive supervision by retrieving sets of diverse views
  of the same object. This powerful paired dataset enables us to train a
  straightforward text-to-image diffusion architecture to map the object and
  scene descriptions to the composited image. We compare our method, ObjectMate,
  with state-of-the-art methods for object insertion and subject-driven
  generation, using a single or multiple references. Empirically, ObjectMate
  achieves superior identity preservation and more photorealistic composition.
  Differently from many other multi-reference methods, ObjectMate does not
  require slow test-time tuning.
paper_authors._id: 675b1648634bd80c4c1d84f8
paper_authors.name: Daniel Winter
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-16T10:16:31.261Z
paper_authors._id: 675fccea42bc7570939a3642
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/x081h2YwSs9kIUgA5me8z.png
paper_authors.fullname: Daniel Winter
paper_authors.type: user
paper_authors.user: danielwinter
paper_authors._id: 675b1648634bd80c4c1d84f9
paper_authors.name: Asaf Shul
paper_authors._id: 675b1648634bd80c4c1d84fa
paper_authors.name: Matan Cohen
paper_authors._id: 675b1648634bd80c4c1d84fb
paper_authors.name: Dana Berman
paper_authors._id: 675b1648634bd80c4c1d84fc
paper_authors.name: Yael Pritch
paper_authors._id: 675b1648634bd80c4c1d84fd
paper_authors.name: Alex Rav-Acha
paper_authors._id: 675b1648634bd80c4c1d84fe
paper_authors.name: Yedid Hoshen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-16T09:42:31.672Z
paper_authors._id: 646cfc3b4220471ca0c56b20
paper_authors.avatarUrl: /avatars/19d6ab141ec2cd25c1c3b45fd8f69910.svg
paper_authors.fullname: Yedid Hoshen
paper_authors.type: user
paper_authors.user: yedid
paper_title: ObjectMate: A Recurrence Prior for Object Insertion and Subject-Driven
    Generation
paper_summary: This paper introduces a tuning-free method for both object insertion and
  subject-driven generation. The task involves composing an object, given
  multiple views, into a scene specified by either an image or text. Existing
  methods struggle to fully meet the task's challenging objectives: (i)
  seamlessly composing the object into the scene with photorealistic pose and
  lighting, and (ii) preserving the object's identity. We hypothesize that
  achieving these goals requires large scale supervision, but manually collecting
  sufficient data is simply too expensive. The key observation in this paper is
  that many mass-produced objects recur across multiple images of large unlabeled
  datasets, in different scenes, poses, and lighting conditions. We use this
  observation to create massive supervision by retrieving sets of diverse views
  of the same object. This powerful paired dataset enables us to train a
  straightforward text-to-image diffusion architecture to map the object and
  scene descriptions to the composited image. We compare our method, ObjectMate,
  with state-of-the-art methods for object insertion and subject-driven
  generation, using a single or multiple references. Empirically, ObjectMate
  achieves superior identity preservation and more photorealistic composition.
  Differently from many other multi-reference methods, ObjectMate does not
  require slow test-time tuning.
paper_ai_keywords: diffusion architecture text-to-image composited image identity preservation photorealistic composition