  We address the problem of generating images across two drastically different
views, namely ground (street) and aerial (overhead) views. Image synthesis by
itself is a very challenging computer vision task and is even more so when
generation is conditioned on an image in another view. Due the difference in
viewpoints, there is small overlapping field of view and little common content
between these two views. Here, we try to preserve the pixel information between
the views so that the generated image is a realistic representation of cross
view input image. For this, we propose to use homography as a guide to map the
images between the views based on the common field of view to preserve the
details in the input image. We then use generative adversarial networks to
inpaint the missing regions in the transformed image and add realism to it. Our
exhaustive evaluation and model comparison demonstrate that utilizing geometry
constraints adds fine details to the generated images and can be a better
approach for cross view image synthesis than purely pixel based synthesis
methods.

Computer Vision and Pattern Recognition