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Deep neural networks have recently been used to edit images with greatsuccess, in particular for faces. However, they are often limited to only beingable to work at a restricted range of resolutions. Many methods are so flexiblethat face edits can often result in an unwanted loss of identity. This workproposes to learn how to perform semantic image edits through the applicationof smooth warp fields. Previous approaches that attempted to use warping forsemantic edits required paired data, i.e. example images of the same subjectwith different semantic attributes. In contrast, we employ recent advances inGenerative Adversarial Networks that allow our model to be trained withunpaired 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 alsoshow that our edits are substantially better at preserving the subject'sidentity. The robustness of our approach is demonstrated by showing plausibleimage editing results on the Cub200 birds dataset. To our knowledge this hasnot been previously accomplished, due the challenging nature of the dataset.

The GAN that Warped: Semantic Attribute Editing with Unpaired Data