  Deep neural networks have recently been used to edit images with great
success, in particular for faces. However, they are often limited to only being
able to work at a restricted range of resolutions. Many methods are so flexible
that face edits can often result in an unwanted loss of identity. This work
proposes to learn how to perform semantic image edits through the application
of smooth warp fields. Previous approaches that attempted to use warping for
semantic edits required paired data, i.e. example images of the same subject
with different semantic attributes. In contrast, we employ recent advances in
Generative Adversarial Networks that allow our model to be trained with
unpaired data. We demonstrate face editing at very high resolutions (4k images)
with a single forward pass of a deep network at a lower resolution. We also
show that our edits are substantially better at preserving the subject's
identity. The robustness of our approach is demonstrated by showing plausible
image editing results on the Cub200 birds dataset. To our knowledge this has
not been previously accomplished, due the challenging nature of the dataset.
