A comprehensive framework of multiple semantics preservation in neural style transfer

Recently neural style transfer has achieved great development, but there is still a big gap compared with manual creation. Most of the existing methods ignore the comprehensive consideration of preserving various semantic information of original content images, resulting in distortion or loss of original content features of the generated works, which are dull and difficult to convey the original themes and emotions. In this paper, we analyze the ability of the existing methods to maintain single semantic information and propose a fast style transfer framework with multi-semantic preservation. The experiments indicate that our method can effectively retain the original semantic information including salience and depth features, so that the final artwork has better visual effect by highlighting its regional focus and depth information. Compared with existing methods, our method has better ability in semantic preservation and can generate more artworks with distinct regions, controllable semantics, diverse contents and rich emotions.