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 |
|---|---|
| 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 |
|---|---|
| Country | Canada |
| City | Vancouver |
| Period | 18/8/12 → 18/8/16 |
Fingerprint
Keywords
- Face
- Face swapping
- Image editing
- Portrait
ASJC Scopus subject areas
- Software
- Computer Graphics and Computer-Aided Design
Cite this
RSGAN : Face swapping and editing using face and hair representation in latent spaces. / Natsume, Ryota; Yatagawa, Tatsuya; Morishima, Shigeo.
ACM SIGGRAPH 2018 Posters, SIGGRAPH 2018. Association for Computing Machinery, Inc, 2018. a69.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - RSGAN
T2 - Face swapping and editing using face and hair representation in latent spaces
AU - Natsume, Ryota
AU - Yatagawa, Tatsuya
AU - Morishima, Shigeo
PY - 2018/8/12
Y1 - 2018/8/12
N2 - 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.
AB - 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.
KW - Face
KW - Face swapping
KW - Image editing
KW - Portrait
UR - http://www.scopus.com/inward/record.url?scp=85054809781&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054809781&partnerID=8YFLogxK
U2 - 10.1145/3230744.3230818
DO - 10.1145/3230744.3230818
M3 - Conference contribution
AN - SCOPUS:85054809781
SN - 9781450358170
BT - ACM SIGGRAPH 2018 Posters, SIGGRAPH 2018
PB - Association for Computing Machinery, Inc
ER -