Real-time arbitrary video style transfer

Abstract

Video style transfer aims to synthesize a stylized video that has similar content structure with a content video and is rendered in the style of a style image. The existing video style transfer methods cannot simultaneously realize high efficiency, arbitrary style and temporal consistency. In this paper, we propose the first real-time arbitrary video style transfer method with only one model. Specifically, we utilize a three-network architecture consisting of a prediction network, a stylization network and a loss network. Prediction network is used for extracting style parameters from a given style image; Stylization network is for generating the corresponding stylized video; Loss network is for training prediction network and stylization network, whose loss function includes content loss, style loss and temporal consistency loss. We conduct three experiments and a user study to test the effectiveness of our method. The experimental results show that our method outperforms the state-of-the-arts.