Multi-granularity Brushstrokes Network for Universal Style Transfer

Abstract

Neural style transfer has been developed in recent years, where both performance and efficiency have been greatly improved. However, most existing methods do not transfer the brushstrokes information of style images well. In this article, we address this issue by training a multi-granularity brushstrokes network based on a parallel coding structure. Specifically, we first adopt the content parsing module to obtain the spatial distribution of content image and the smoothness of different regions. Then, different brushstrokes features are transformed by a multi-granularity style-swap module guided by the region content map. Finally, the stylized features of the two branches are fused to enhance the stylized results. The multi-granularity brushstrokes network is jointly supervised by a new multi-layer brushstroke loss and pre-existing loss. The proposed method is close to the artistic drawing process. In addition, we can control whether the color of the stylized results tend to be the style image or the content image. Experimental results demonstrate the advantage of our proposed method compare with the existing schemes.