OmniPaint: Mastering Object-Oriented Editing via Disentangled
  Insertion-Removal Inpainting
Authors: name: Yongsheng Yu, name: Ziyun Zeng, name: Haitian Zheng, name: Jiebo Luo

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/