  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.
