Multi-layer feature fusion based image style transfer with arbitrary text condition

Abstract

Style transfer refers to the conversion of images in two different domains. Compared with the style transfer based on the style image, the image style transfer through the text description is more free and applicable to more practical scenarios. However, the image style transfer method under the text condition needs to be trained and optimized for different text and image inputs each time, resulting in limited style transfer efficiency. Therefore, this paper proposes a multi-layer feature fusion based style transfer method (MlFFST) with arbitrary text condition. To address the problems of distortion and missing semantic content, we also introduce a multi-layer attention normalization module. The experimental results show that the method in this paper can generate stylized results with high quality, good effect and high stability for images and videos. And this method can meet real-time requirements to generate more artistic and aesthetic images and videos.