(A) study on the creation of various images and videos based on user input using deep learning networks

Abstract

This paper aims to bridge the gap between deep learning technology and visual content creation by proposing several methods for generating images and videos based on various types of user input through generative adversarial networks. Recently, deep neural networks have shown improved performance and rapid development in many fields. Specifically, generative adversarial networks that utilize the adversarial learning of two neural networks, a generator and a discriminator, produce very diverse and realistic result images. This work mainly consists of two major generative tasks. First, we propose a conditional video learning methodology based on adversarial neural networks. Second, we suggest new approaches to transferring real-world images and videos to the artistic domain. We show the effectiveness of the proposed methods by conducting extensive qualitative and quantitative experiments on diverse datasets.