Joint Sketch-Attribute Learning for Fine-Grained Face Synthesis

Binxin Yang, Xuejin Chen, Richang Hong, Zihan Chen, Yuhang Li, Zheng-Jun Zha

Abstract: The photorealism of synthetic face images has been significantly improved by generative adversarial networks (GANs). Besides of the realism, more accurate control on the properties of face images. While sketches convey the desired shapes, attributes describe appearance. However, it remains challenging to jointly exploit sketches and attributes, which are in different modalities, to generate high-resolution photorealistic face images. In this paper, we propose a novel joint sketch-attribute learning approach to synthesize photo-realistic face images with conditional GANs. A hybrid generator is proposed to learn a unified embedding of shape from sketches and appearance from attributes for synthesizing images. We propose an attribute modulation module, which transfers user-preferred attributes to reinforce sketch representation with appearance details. Using the proposed approach, users could flexibly manipulate the desired shape and appearance of synthesized face images with fine-grained control. We conducted extensive experiments on the CelebA-HQ dataset [16]. The experimental results have demonstrated the effectiveness of the proposed approach.