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A GAN-Based Face Rotation for Artistic Portraits

Author

Listed:
  • Handong Kim

    (Department of Computer Science, Sangmyung University, Seoul 03016, Korea
    These authors contributed equally to this work.)

  • Junho Kim

    (Department of Computer Science, Sangmyung University, Seoul 03016, Korea
    These authors contributed equally to this work.)

  • Heekyung Yang

    (Division of SW Convergence, Sangmyung University, Seoul 03016, Korea)

Abstract

We present a GAN-based model that rotates the faces in artistic portraits to various angles. We build a dataset of artistic portraits for training our GAN-based model by applying a 3D face model to the artistic portraits. We also devise proper loss functions to preserve the styles in the artistic portraits as well as to rotate the faces in the portraits to proper angles. These approaches enable us to construct a GAN-based face rotation model. We apply this model to various artistic portraits, including photorealistic oil paint portraits, watercolor portraits, well-known portrait artworks and banknote portraits, and produce convincing rotated faces in the artistic portraits. Finally, we prove that our model can produce improved results compared with the existing models by evaluating the similarity and the angles of the rotated faces through evaluation schemes including FID estimation, recognition ratio estimation, pose estimation and user study.

Suggested Citation

  • Handong Kim & Junho Kim & Heekyung Yang, 2022. "A GAN-Based Face Rotation for Artistic Portraits," Mathematics, MDPI, vol. 10(20), pages 1-20, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3860-:d:945560
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