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Biphasic face photo-sketch synthesis has significant practical value inwide-ranging fields such as digital entertainment and law enforcement. Previousapproaches directly generate the photo-sketch in a global view, they alwayssuffer from the low quality of sketches and complex photo variations, leadingto unnatural and low-fidelity results. In this paper, we propose a novelSemantic-Driven Generative Adversarial Network to address the above issues,cooperating with Graph Representation Learning. Considering that human faceshave distinct spatial structures, we first inject class-wise semantic layoutsinto the generator to provide style-based spatial information for synthesizedface photos and sketches. Additionally, to enhance the authenticity of detailsin generated faces, we construct two types of representational graphs viasemantic parsing maps upon input faces, dubbed the IntrA-class Semantic Graph(IASG) and the InteR-class Structure Graph (IRSG). Specifically, the IASGeffectively models the intra-class semantic correlations of each facialsemantic component, thus producing realistic facial details. To preserve thegenerated faces being more structure-coordinated, the IRSG models inter-classstructural relations among every facial component by graph representationlearning. To further enhance the perceptual quality of synthesized images, wepresent a biphasic interactive cycle training strategy by fully takingadvantage of the multi-level feature consistency between the photo and sketch.Extensive experiments demonstrate that our method outperforms thestate-of-the-art competitors on the CUFS and CUFSF datasets.

Biphasic Face Photo-Sketch Synthesis via Semantic-Driven Generative Adversarial Network with Graph Representation Learning