Cross-Domain Face Sketch Synthesis

Synthesizing sketches from facial photos is of great significance to digital entertainment. Along with higher demands on sketch quality in a complex environment, however, it has been an urgent issue on how to synthesize realistic sketches with the limited training data. The existing face sketch methods pay less attention to the insufficient problem of the training data, leading to the synthesized sketches with some noise or without some identity-specific information in real-world applications. Target on providing sufficient photo-sketch pairs to meet the demand of users in digital entertainment, we present a cross-domain face sketch synthesis framework in this paper. In the photo-sketch mixed domain, we leverage the generative adversarial network to construct a cross-domain mapping function and generate identity-preserving face sketches as the hidden training data. Combined it with the insufficient original training data, we provide sufficient training data to recover the underlying structures and learn the cross-domain transfer of the high-level qualitative knowledge from the photo domain to the sketch domain by the latent low-rank representation. The qualitative and quantitative evaluations on the public facial photo-sketch database demonstrate that the proposed cross-domain face sketch synthesis method can solve the insufficient problem of the training data successfully. And it outperforms other state-of-the-art works and generates more vivid and cleaner facial sketches.