부산대학교 도서관

This paper proposes a novel inpainting framework, omission-free inpainting, which ensures generating the desired object in the masked region. Despite recent advancements in text-driven and class-conditional inpainting models, they often fail to restore the missing object. To address this issue, the proposed framework includes a separate object generation stage, resulting in omission-free inpainting. The framework consists of three stages: background generation, object generation and refinement. The background generation stage restores a harmonious background with the surrounding pixels, while the object generation stage creates the desired object using a blending mask that allows the object to be influenced by the background’s color and brightness. Finally, the refinement stage blends the object and background to produce a visually realistic image. We compare the results qualitatively with the state-of-the-art methods, and our method outperforms the existing methods in CLIP score.