  Deep learning-based image inpainting algorithms have shown great performance
via powerful learned prior from the numerous external natural images. However,
they show unpleasant results on the test image whose distribution is far from
the that of training images because their models are biased toward the training
images. In this paper, we propose a simple image inpainting algorithm with
test-time adaptation named AdaFill. Given a single out-of-distributed test
image, our goal is to complete hole region more naturally than the pre-trained
inpainting models. To achieve this goal, we treat remained valid regions of the
test image as another training cues because natural images have strong internal
similarities. From this test-time adaptation, our network can exploit
externally learned image priors from the pre-trained features as well as the
internal prior of the test image explicitly. Experimental results show that
AdaFill outperforms other models on the various out-of-distribution test
images. Furthermore, the model named ZeroFill, that are not pre-trained also
sometimes outperforms the pre-trained models.
