Edge Aware Domain Transformation for Face Sketch Synthesis

Abstract

With the development of generative adversarial networks (GAN), the field of face sketch synthesis has received extensive attention. Face sketch synthesis (FSS) has promising prospects in the fields of entertainment and law enforcement, where it plays an increasingly important role. We propose a novel generative adversarial network for synthesizing sketches with similar shapes and rich details to photos. This problem is challenging because it involves the transition between the sketch domain and the photo domain. Many methods have been used for face sketch synthesis in recent years, but existing methods cannot fully exploit the semantic information between different domains. To this end, we use a cross-domain face sketch synthesis framework based on edge-preserving filters to make the boundaries of different semantics in semantic layouts have a smooth transition. We further propose a new spatially adaptive denormalization module named edge-aware enhancement Spatially Adaptive DEnormalization (eaeSPADE), which can make full use of the semantic information in the semantic layout of faces and improve the details of the synthesized face images. Extensive experiments demonstrate that our method outperforms existing face sketch synthesis methods.