The average time spent watching online videos increases every year, across all demographics. Videos are more engaging and are shared twice as much as other types of media. However, making or editing such videos can be expensive and time-consuming. Our research goal is to propose solutions based on machine learning and computational aesthetics to automate steps in the creation and editing of videos that are appealing and of interest for the viewer. In this proposal, we discuss three existing works and how they relate to our research. We first examine how generative adversarial networks (GANs) can be used to generate videos and what are their limitations. Then, we take a look at an example of data collection and annotation process, allowing training of models for video aesthetics and message understanding. Finally, we discuss a framework to navigate GANs’ latent space to improve aesthetics.