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