FSNet: An Identity-Aware Generative Model for Image-Based Face Swapping

Ryota Natsume, Tatsuya Yatagawa, Shigeo Morishima

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper presents FSNet, a deep generative model for image-based face swapping. Traditionally, face-swapping methods are based on three-dimensional morphable models (3DMMs), and facial textures are replaced between the estimated three-dimensional (3D) geometries in two images of different individuals. However, the estimation of 3D geometries along with different lighting conditions using 3DMMs is still a difficult task. We herein represent the face region with a latent variable that is assigned with the proposed deep neural network (DNN) instead of facial textures. The proposed DNN synthesizes a face-swapped image using the latent variable of the face region and another image of the non-face region. The proposed method is not required to fit to the 3DMM; additionally, it performs face swapping only by feeding two face images to the proposed network. Consequently, our DNN-based face swapping performs better than previous approaches for challenging inputs with different face orientations and lighting conditions. Through several experiments, we demonstrated that the proposed method performs face swapping in a more stable manner than the state-of-the-art method, and that its results are compatible with the method thereof.

Original languageEnglish
Title of host publicationComputer Vision – ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers
EditorsGreg Mori, Hongdong Li, Konrad Schindler, C.V. Jawahar
PublisherSpringer Verlag
Pages117-132
Number of pages16
ISBN (Print)9783030208752
DOIs
Publication statusPublished - 2019
Event14th Asian Conference on Computer Vision, ACCV 2018 - Perth, Australia
Duration: 2018 Dec 22018 Dec 6

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11366 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th Asian Conference on Computer Vision, ACCV 2018
CountryAustralia
CityPerth
Period18/12/218/12/6

Keywords

  • Convolutional neural networks
  • Deep generative models
  • Face swapping

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Natsume, R., Yatagawa, T., & Morishima, S. (2019). FSNet: An Identity-Aware Generative Model for Image-Based Face Swapping. In G. Mori, H. Li, K. Schindler, & C. V. Jawahar (Eds.), Computer Vision – ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers (pp. 117-132). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11366 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-20876-9_8