Video Cartoonization based on Generative Adversarial Networks

Abstract

Animation has been playing an important role in the economy and culture. However, making animation is a hard work and costly. To solve the problem, we propose new solution for animation video generative model, which of baseline is Cartoon-GAN. Cartoon-GAN is a kind of image to image style transfer network, and it has attained to generate cartoon style image from real-world scenes. In deep learning, there are usually two ways to process videos: 3d convolution or 2d convolution added with temporal processing. However, existing method doesn’t achieve enough smoothness in the cartoon-style video making task. For our cartoon-style transfer task in video to video, our new solution is to use each two image frames and optical flow as an input for the generator. In this paper, we generated cartoon videos by adopting optical flow, which is effective to predict object motion.