Facial attribute-controlled sketch-to-image translation with generative adversarial networks
Mingming Hu, Jingtao Guo
Abstract
Abstract Due to the rapid development of the generative adversarial networks (GANs) and convolution neural networks (CNN), increasing attention is being paid to face synthesis. In this paper, we address the new and challenging task of facial sketch-to-image synthesis with multiple controllable attributes. To achieve this goal, first, we propose a new attribute classification loss to ensure that the synthesized face image with the facial attributes, which the users desire to have. Second, we employ the reconstruction loss to synthesize the facial texture and structure information. Third, the adversarial loss is used to encourage visual authenticity. By incorporating above losses into a unified framework, our proposed method not only can achieve high-quality sketch-to-image translation, but also allow the users control the facial attributes of synthesized image. Extensive experiments show that user-provided facial attribute information effectively controls the process of facial sketch-to-image translation.