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

    2 引用 (Scopus)

    抜粋

    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.

    元の言語English
    ホスト出版物のタイトルACM SIGGRAPH 2018 Posters, SIGGRAPH 2018
    出版者Association for Computing Machinery, Inc
    ISBN(印刷物)9781450358170
    DOI
    出版物ステータス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

    Other

    OtherACM SIGGRAPH 2018 Posters - International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2018
    Canada
    Vancouver
    期間18/8/1218/8/16

    ASJC Scopus subject areas

    • Software
    • Computer Graphics and Computer-Aided Design

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  • これを引用

    Natsume, R., Yatagawa, T., & Morishima, S. (2018). RSGAN: Face swapping and editing using face and hair representation in latent spaces. : ACM SIGGRAPH 2018 Posters, SIGGRAPH 2018 [a69] Association for Computing Machinery, Inc. https://doi.org/10.1145/3230744.3230818