In recent years, significant progress has been achieved in face image editing due to the success of Generative Adversarial Network (GAN). However, state-of-the-art face editing methods mainly suffer from the following two limitations: 1) they are only applicable to face images with relative low-resolutions and 2) multi-attribute face editing may generate uncontrollable changes in non-target face attribute categories. To solve these problems, we propose a novel High-Quality Generative Adversarial Network (HQ-GAN) for controllable editing of multiple face attributes in high-resolution images. HQ-GAN has two novel ideas to break the limitations of resolution and controllability correspondingly: 1) fine-grained textures and realistic details of high-resolution face images are better preserved with the aid of textural features extracted by the wavelet transform module and 2) desired multi-attribute targets of face editing are emphasized using a weighted binary cross-entropy (BCE) loss so that the influence on non-target attributes is greatly reduced. To the best of our knowledge, HQ-GAN is the first attempt to achieve continuous editing of multiple face attributes on high-resolution images of the CelebA-HQ using only 28 000 training samples. Extensive qualitative results demonstrate the superiority of the proposed method in rendering realistic high-resolution face images with accurate attribute modification, and comprehensive quantitative results show that the proposed method significantly outperforms state-of-the-art face editing methods.

In recent years, significant progress has been achieved in face image editing due to the success of Generative Adversarial Network (GAN). However, state-of-the-art face editing methods mainly suffer from the following two limitations: 1) they are only applicable to face images with relative low-resolutions and 2) multi-attribute face editing may generate uncontrollable changes in non-target face attribute categories. To solve these problems, we propose a novel High-Quality Generative Adversarial Network (HQ-GAN) for controllable editing of multiple face attributes in high-resolution images. HQ-GAN has two novel ideas to break the limitations of resolution and controllability correspondingly: 1) fine-grained textures and realistic details of high-resolution face images are better preserved with the aid of textural features extracted by the wavelet transform module and 2) desired multi-attribute targets of face editing are emphasized using a weighted binary cross-entropy (BCE) loss so that the influence on non-target attributes is greatly reduced. To the best of our knowledge, HQ-GAN is the first attempt to achieve continuous editing of multiple face attributes on high-resolution images of the CelebA-HQ using only 28 000 training samples. Extensive qualitative results demonstrate the superiority of the proposed method in rendering realistic high-resolution face images with accurate attribute modification, and comprehensive quantitative results show that the proposed method significantly outperforms state-of-the-art face editing methods.