Automatically manipulating facial attributes is challenging because it needs to modify the facial appearances, while keeping not only the person's identity but also the realism of the resultant images. Unlike the prior works on the facial attribute parsing, we aim at an inverse and more challenging problem called attribute manipulation by modifying a facial image in line with a reference facial attribute. Given a source input image and reference images with a target attribute, our goal is to generate a new image (i.e., target image) that not only possesses the new attribute but also keeps the same or similar content with the source image. In order to generate new facial attributes, we train a deep neural network with a combination of a perceptual content loss and two adversarial losses, which ensure the global consistency of the visual content while implementing the desired attributes often impacting on local pixels. The model automatically adjusts the visual attributes on facial appearances and keeps the edited images as realistic as possible. The evaluation shows that the proposed model can provide a unified solution to both local and global facial attribute manipulation such as expression change and hair style transfer. Moreover, we further demonstrate that the learned attribute discriminator can be used for attribute localization.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781538648865
StatePublished - May 3 2018
Event18th IEEE Winter Conference on Applications of Computer Vision, WACV 2018 - Lake Tahoe, United States
Duration: Mar 12 2018Mar 15 2018


Other18th IEEE Winter Conference on Applications of Computer Vision, WACV 2018
CountryUnited States
CityLake Tahoe

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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