title: OmniPaint: Mastering Object-Oriented Editing via Disentangled
    Insertion-Removal Inpainting
summary: Diffusion-based generative models have revolutionized object-oriented image
  editing, yet their deployment in realistic object removal and insertion remains
  hampered by challenges such as the intricate interplay of physical effects and
  insufficient paired training data. In this work, we introduce OmniPaint, a
  unified framework that re-conceptualizes object removal and insertion as
  interdependent processes rather than isolated tasks. Leveraging a pre-trained
  diffusion prior along with a progressive training pipeline comprising initial
  paired sample optimization and subsequent large-scale unpaired refinement via
  CycleFlow, OmniPaint achieves precise foreground elimination and seamless
  object insertion while faithfully preserving scene geometry and intrinsic
  properties. Furthermore, our novel CFD metric offers a robust, reference-free
  evaluation of context consistency and object hallucination, establishing a new
  benchmark for high-fidelity image editing. Project page:
  https://yeates.github.io/OmniPaint-Page/
paper_authors._id: 67d3007727a57c422730858e
paper_authors.name: Yongsheng Yu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-14T08:57:07.420Z
paper_authors._id: 635b6bdfd63bcb528acefa78
paper_authors.avatarUrl: /avatars/54c62b41c147d0450bdfc72121860bb4.svg
paper_authors.fullname: Yongsheng Yu
paper_authors.type: user
paper_authors.user: yeates
paper_authors._id: 67d3007727a57c422730858f
paper_authors.name: Ziyun Zeng
paper_authors._id: 67d3007727a57c4227308590
paper_authors.name: Haitian Zheng
paper_authors._id: 67d3007727a57c4227308591
paper_authors.name: Jiebo Luo
paper_title: OmniPaint: Mastering Object-Oriented Editing via Disentangled
    Insertion-Removal Inpainting
paper_summary: Diffusion-based generative models have revolutionized object-oriented image
  editing, yet their deployment in realistic object removal and insertion remains
  hampered by challenges such as the intricate interplay of physical effects and
  insufficient paired training data. In this work, we introduce OmniPaint, a
  unified framework that re-conceptualizes object removal and insertion as
  interdependent processes rather than isolated tasks. Leveraging a pre-trained
  diffusion prior along with a progressive training pipeline comprising initial
  paired sample optimization and subsequent large-scale unpaired refinement via
  CycleFlow, OmniPaint achieves precise foreground elimination and seamless
  object insertion while faithfully preserving scene geometry and intrinsic
  properties. Furthermore, our novel CFD metric offers a robust, reference-free
  evaluation of context consistency and object hallucination, establishing a new
  benchmark for high-fidelity image editing. Project page:
  https://yeates.github.io/OmniPaint-Page/
paper_ai_keywords: diffusion-based generative models OmniPaint diffusion prior CycleFlow foreground elimination seamless object insertion scene geometry intrinsic properties CFD metric context consistency object hallucination high-fidelity image editing