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- W4385801422 abstract "Image completion is widely used in photo restoration and editing applications, e.g. for object removal. Recently, there has been a surge of research on generating diverse completions for missing regions. However, existing methods require large training sets from a specific domain of interest, and often fail on general-content images. In this paper, we propose a diverse completion method that does not require a training set and can thus treat arbitrary images from any domain. Our internal diverse completion (IDC) approach draws inspiration from recent single-image generative models that are trained on multiple scales of a single image, adapting them to the extreme setting in which only a small portion of the image is available for training. We illustrate the strength of IDC on several datasets, using both user studies and quantitative comparisons." @default.
- W4385801422 created "2023-08-15" @default.
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- W4385801422 date "2023-06-01" @default.
- W4385801422 modified "2023-09-27" @default.
- W4385801422 title "Internal Diverse Image Completion" @default.
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- W4385801422 doi "https://doi.org/10.1109/cvprw59228.2023.00072" @default.
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