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      VecGAN: Image-to-Image translation with interpretable latent directions

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      Author(s)
      Dalva, Yusuf
      Dundar, Aysegul
      Altındiş, Said Fahri
      Date
      2022-10-21
      Source Title
      Computer Vision – ECCV 2022
      Print ISSN
      03029743
      Volume
      13676
      Pages
      153 - 169
      Language
      English
      Type
      Article
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      Abstract
      We propose VecGAN, an image-to-image translation framework for facial attribute editing with interpretable latent directions. Facial attribute editing task faces the challenges of precise attribute editing with controllable strength and preservation of the other attributes of an image. For this goal, we design the attribute editing by latent space factorization and for each attribute, we learn a linear direction that is orthogonal to the others. The other component is the controllable strength of the change, a scalar value. In our framework, this scalar can be either sampled or encoded from a reference image by projection. Our work is inspired by the latent space factorization works of fixed pretrained GANs. However, while those models cannot be trained end-to-end and struggle to edit encoded images precisely, VecGAN is end-to-end trained for image translation task and successful at editing an attribute while preserving the others. Our extensive experiments show that VecGAN achieves significant improvements over state-of-the-arts for both local and global edits.
      Keywords
      Image translation
      Generative adversarial networks
      Latent space manipulation
      Face attribute editing
      Permalink
      http://hdl.handle.net/11693/111419
      Published Version (Please cite this version)
      https://www.doi.org/10.1007/978-3-031-19787-1_9
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      • Department of Computer Engineering 1561
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