  Learning to generate natural scenes has always been a daunting task in
computer vision. This is even more laborious when generating images with very
different views. When the views are very different, the view fields have little
overlap or objects are occluded, leading the task very challenging. In this
paper, we propose to use Generative Adversarial Networks(GANs) based on a
deformable convolution and attention mechanism to solve the problem of
cross-view image synthesis (see Fig.1). It is difficult to understand and
transform scenes appearance and semantic information from another view, thus we
use deformed convolution in the U-net network to improve the network's ability
to extract features of objects at different scales. Moreover, to better learn
the correspondence between images from different views, we apply an attention
mechanism to refine the intermediate feature map thus generating more realistic
images. A large number of experiments on different size images on the Dayton
dataset[1] show that our model can produce better results than state-of-the-art
methods.
