Deep Generative Multimedia Children's Literature
Matthew L. Olson

TL;DR
This paper presents a system that uses pretrained deep neural networks to create multimedia children's literature, demonstrating the potential and challenges of AI-generated creative content across multiple media domains.
Contribution
It introduces a novel approach for generating multimedia children's stories using only publicly available pretrained models, highlighting cross-domain creative AI applications.
Findings
Generated stories showcase diverse multimedia content.
The system demonstrates feasibility of AI in children's literature.
Identifies challenges in AI-driven creative multimedia work.
Abstract
Artistic work leveraging Machine Learning techniques is an increasingly popular endeavour for those with a creative lean. However, most work is done in a single domain: text, images, music, etc. In this work, I design a system for a machine learning created multimedia experience, specifically in the genre of children's literature. We detail the process for exclusively using publicly available pretrained deep neural network based models, I present multiple examples of the work my system creates, and I explore the problems associated in this area of creative work.
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Taxonomy
TopicsMusic and Audio Processing · Human Motion and Animation · Generative Adversarial Networks and Image Synthesis
