The GAN that warped: semantic attribute editing with unpaired data

Dorta, Garoe, Vicente, Sara, Campbell, Neill D F and Simpson, Ivor J A (2020) The GAN that warped: semantic attribute editing with unpaired data. Computer Vision and Pattern Recognition (CVPR) 2020, Seattle, USA, 16 - 20 June, 2020. Published in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Xplore ISSN 1063-6919 ISBN 9781728171692

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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.

Item Type: Conference Proceedings
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Schools and Departments: School of Engineering and Informatics > Informatics
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Date Deposited: 11 Mar 2020 08:11
Last Modified: 18 Feb 2022 12:06

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