  Face sketch synthesis has made significant progress with the development of
deep neural networks in these years. The delicate depiction of sketch portraits
facilitates a wide range of applications like digital entertainment and law
enforcement. However, accurate and realistic face sketch generation is still a
challenging task due to the illumination variations and complex backgrounds in
the real scenes. To tackle these challenges, we propose a novel Semantic-Driven
Generative Adversarial Network (SDGAN) which embeds global structure-level
style injection and local class-level knowledge re-weighting. Specifically, we
conduct facial saliency detection on the input face photos to provide overall
facial texture structure, which could be used as a global type of prior
information. In addition, we exploit face parsing layouts as the semantic-level
spatial prior to enforce globally structural style injection in the generator
of SDGAN. Furthermore, to enhance the realistic effect of the details, we
propose a novel Adaptive Re-weighting Loss (ARLoss) which dedicates to balance
the contributions of different semantic classes. Experimentally, our extensive
experiments on CUFS and CUFSF datasets show that our proposed algorithm
achieves state-of-the-art performance.
