Abstract
This abstract introduces a generative neural network for face swapping and editing face images. We refer to this network as "region-separative generative adversarial network (RSGAN)". In existing deep generative models such as Variational autoencoder (VAE) and Generative adversarial network (GAN), training data must represent what the generative models synthesize. For example, image inpainting is achieved by training images with and without holes. However, it is difficult or even impossible to prepare a dataset which includes face images both before and after face swapping because faces of real people cannot be swapped without surgical operations. We tackle this problem by training the network so that it synthesizes synthesize a natural face image from an arbitrary pair of face and hair appearances. In addition to face swapping, the proposed network can be applied to other editing applications, such as visual attribute editing and random face parts synthesis.
Original language | English |
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Title of host publication | ACM SIGGRAPH 2018 Posters, SIGGRAPH 2018 |
Publisher | Association for Computing Machinery, Inc |
ISBN (Print) | 9781450358170 |
DOIs | |
Publication status | Published - 2018 Aug 12 |
Event | ACM SIGGRAPH 2018 Posters - International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2018 - Vancouver, Canada Duration: 2018 Aug 12 → 2018 Aug 16 |
Other
Other | ACM SIGGRAPH 2018 Posters - International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2018 |
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Country | Canada |
City | Vancouver |
Period | 18/8/12 → 18/8/16 |
Keywords
- Face
- Face swapping
- Image editing
- Portrait
ASJC Scopus subject areas
- Software
- Computer Graphics and Computer-Aided Design