  In this paper, we propose a solution to transform a video into a comics. We
approach this task using a neural style algorithm based on Generative
Adversarial Networks (GANs). Several recent works in the field of Neural Style
Transfer showed that producing an image in the style of another image is
feasible. In this paper, we build up on these works and extend the existing set
of style transfer use cases with a working application of video comixification.
To that end, we train an end-to-end solution that transforms input video into a
comics in two stages. In the first stage, we propose a state-of-the-art
keyframes extraction algorithm that selects a subset of frames from the video
to provide the most comprehensive video context and we filter those frames
using image aesthetic estimation engine. In the second stage, the style of
selected keyframes is transferred into a comics. To provide the most
aesthetically compelling results, we selected the most state-of-the art style
transfer solution and based on that implement our own ComixGAN framework. The
final contribution of our work is a Web-based working application of video
comixification available at http://comixify.ii.pw.edu.pl.
