A Content Transformation Block For Image Style Transfer
This work addresses the challenge of content-aware stylization for applications in image synthesis and video processing, representing an incremental improvement over existing style transfer methods.
The paper tackles the problem of image style transfer by explicitly transforming image content details, which previous methods neglected, and introduces a content transformation module and normalization layer that enable real-time high-definition video stylization.
Style transfer has recently received a lot of attention, since it allows to study fundamental challenges in image understanding and synthesis. Recent work has significantly improved the representation of color and texture and computational speed and image resolution. The explicit transformation of image content has, however, been mostly neglected: while artistic style affects formal characteristics of an image, such as color, shape or texture, it also deforms, adds or removes content details. This paper explicitly focuses on a content-and style-aware stylization of a content image. Therefore, we introduce a content transformation module between the encoder and decoder. Moreover, we utilize similar content appearing in photographs and style samples to learn how style alters content details and we generalize this to other class details. Additionally, this work presents a novel normalization layer critical for high resolution image synthesis. The robustness and speed of our model enables a video stylization in real-time and high definition. We perform extensive qualitative and quantitative evaluations to demonstrate the validity of our approach.