Internal Diverse Image Completion

• URL: http://arxiv.org/abs/2212.10280v1
• Date: Sun, 18 Dec 2022 10:02:53 GMT
• Title: Internal Diverse Image Completion
• Authors: Noa Alkobi, Tamar Rott Shaham, Tomer Michaeli
• Abstract summary: We propose a diverse completion method that does not require a training set and can 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.
• Score: 38.068971605321096
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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