Interactive Generative Adversarial Networks with High-Frequency Compensation for Facial Attribute Editing

Recently, facial attribute editing has drawn increasing attention and has achieved significant progress due to Generative Adversarial Network (GAN). Since paired images before and after editing are not available, existing methods typically perform the editing and reconstruction tasks simultaneously, and transfer facial details learned from the reconstruction to the editing via sharing the latent representation space and weights. In this way, they can not preserve those non-targeted regions well during editing. In addition, they usually introduce skip connections between the encoder and decoder to improve image quality at the cost of attribute editing ability. In this paper, we propose a novel method called InterGAN with high-frequency compensation to alleviate above problems. Specifically, we first propose the cross-task interaction (CTI) to fully explore the relationships between editing and reconstruction tasks. The CTI includes two translations: style translation adjusts the mean and variance of feature maps according to style features, and conditional translation utilizes attribute vector as condition to guide feature map transformation. They provide effective information interaction to preserve the irrelevant regions unchanged. Without using skip connections between the encoder and decoder, furthermore, we propose the high-frequency compensation module (HFCM) to improve image quality. The HFCM tries to collect potentially loss information from input images and each down-sampling layers of the encoder, and then re-inject them into subsequent layers to alleviate the information loss. Ablation analysis show the effectiveness of proposed CTI and HFCM. Extensive qualitative and quantitative experiments on CelebA-HQ demonstrate that the proposed method outperforms state-of-the-art methods both in attribute editing accuracy and image quality.