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Comparing to image inpainting, image outpainting receives less attention dueto two challenges in it. The first challenge is how to keep the spatial andcontent consistency between generated images and original input. The secondchallenge is how to maintain high quality in generated results, especially formulti-step generations in which generated regions are spatially far away fromthe initial input. To solve the two problems, we devise some innovativemodules, named Skip Horizontal Connection and Recurrent Content Transfer, andintegrate them into our designed encoder-decoder structure. By this design, ournetwork can generate highly realistic outpainting prediction effectively andefficiently. Other than that, our method can generate new images with very longsizes while keeping the same style and semantic content as the given input. Totest the effectiveness of the proposed architecture, we collect a new scenerydataset with diverse, complicated natural scenes. The experimental results onthis dataset have demonstrated the efficacy of our proposed network. The codeand dataset are available from https://github.com/z-x-yang/NS-Outpainting.

Very Long Natural Scenery Image Prediction by Outpainting