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:this https URL
View on arXiv@article{yu2025_2503.08677,
title={ OmniPaint: Mastering Object-Oriented Editing via Disentangled Insertion-Removal Inpainting },
author={ Yongsheng Yu and Ziyun Zeng and Haitian Zheng and Jiebo Luo },
journal={arXiv preprint arXiv:2503.08677},
year={ 2025 }
}