In our talk, we will present a Web-based working solution for video comixification - a task of converting a video into a comics. We split this task into two separate problems: (a) frame extraction and (b) style transfer. To extract meaningful, representative frames from the video we employ a keyframe extraction algorithm based on Reinforcement Learning, while for transferring the style into comics we implement a generative adversarial network (GANs) model. Since there have been many works published on the so-called neural style transfer, we evaluate them all on the very same task, namely frame comixification and select the most appropriate method. We examined different combinations of Adaptive Instance Normalization, Universal Style Transfer and GAN models and confront them to find their advantages and disadvantages in terms of qualitative and quantitative analysis. In the talk we will disclose technical details of how our comixification engine works and, finally, we will give a first public presentation of a working video comixification demo available at comixify.ii.pw.edu.pl.