In order to transform videos into comic-style images we present a neural style method in this paper that is powered by Generative Adversarial Networks (GANs). We extend these techniques to the realm of video comixification leveraging recent advancements in Neural Style Transfer that demonstrate the ability to apply the style of one image to another. Our approach is a two-stage end-to-end pipeline that converts input videos into comics. To guarantee the most comprehensive representation of the videos context we first present a cutting-edge keyframe extraction method that selects a subset of frames. These frames are further improved through the use of an image aesthetic evaluation engine. In the second step style transfer is applied to the selected keyframes to make them appear humorous. For the best aesthetic results we develop our own framework ComixGAN and incorporate the most recent style transfer methods. Comixification can be accomplished more dynamically and adaptably by employing GANs for style transfer which enables the comic style to be modified to accommodate various artistic preferences. The style transfer may be tailored to various genres and narrative requirements because to the ComixGAN framework’s ability to handle a broad variety of video content, from action-packed sequences to more still, dialogue-driven moments. Both novice users and experts wishing to experiment with new visual content formats can benefit from our solution’s ability to incorporate these developments into an intuitive web-based application, which creates new opportunities for creative expression and multimedia storytelling. An inventive and aesthetically pleasing solution for video-to-comic transition is provided by the finished product, a fully working web-based application that can turn videos into comics.