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Abstract

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, \ie 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.
Original languageEnglish
Publication statusAcceptance date - 2020
EventIEEE Conference on Computer Vision and Pattern Recognition : CVPR - Seattle, USA United States
Duration: 14 Jun 202019 Jun 2020
http://cvpr2020.thecvf.com/

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition
CountryUSA United States
CitySeattle
Period14/06/2019/06/20
Internet address

Keywords

  • Deep Neural Networks
  • Generative Models
  • GAN

Projects

EPSRC Centre for Doctoral Training in Digital Entertainment

Willis, P.

1/04/1430/09/22

Project: Research-related funding

Cite this

Dorta, G., Vicente, S., Campbell, N., & Simpson, I. (Accepted/In press). The GAN that Warped: Semantic Attribute Editing with Unpaired Data. Paper presented at IEEE Conference on Computer Vision and Pattern Recognition , Seattle, USA United States.