OmniPaint: Mastering Object-Oriented Editing via Disentangled
  Insertion-Removal Inpainting
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/
67d3007727a57c422730858e
Yongsheng Yu
claimed_verified
2025-03-14T08:57:07.420Z
635b6bdfd63bcb528acefa78
/avatars/54c62b41c147d0450bdfc72121860bb4.svg
Yongsheng Yu
user
yeates
67d3007727a57c422730858f
Ziyun Zeng
67d3007727a57c4227308590
Haitian Zheng
67d3007727a57c4227308591
Jiebo Luo
OmniPaint: Mastering Object-Oriented Editing via Disentangled
  Insertion-Removal Inpainting
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/
OmniPaint, utilizing a unified framework with a diffusion prior and CycleFlow, achieves high-fidelity object removal and insertion while maintaining scene geometry and object properties.
diffusion-based generative models object-oriented image editing OmniPaint diffusion prior CycleFlow foreground elimination object insertion scene geometry intrinsic properties CFD metric context consistency object hallucination