RSGAN: Face swapping and editing using face and hair representation in latent spaces

Ryota Natsume, Tatsuya Yatagawa, Shigeo Morishima

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルACM SIGGRAPH 2018 Posters, SIGGRAPH 2018
出版社Association for Computing Machinery, Inc
ISBN(印刷版)9781450358170
DOI
出版ステータスPublished - 2018 8 12
イベントACM SIGGRAPH 2018 Posters - International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2018 - Vancouver, Canada
継続期間: 2018 8 122018 8 16

出版物シリーズ

名前ACM SIGGRAPH 2018 Posters, SIGGRAPH 2018

Other

OtherACM SIGGRAPH 2018 Posters - International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2018
CountryCanada
CityVancouver
Period18/8/1218/8/16

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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