Automated visual story generation aims to produce stories with corresponding illustrations that exhibit coherence, progression, and adherence to characters' emotional development. This work proposes a story generation pipeline to co-create visual stories with the users. The pipeline allows the user to control events and emotions on the generated content. The pipeline includes two parts: narrative and image generation. For narrative generation, the system generates the next sentence using user-specified keywords and emotion labels. For image generation, diffusion models are used to create a visually appealing image corresponding to each generated sentence. Further, object recognition is applied to the generated images to allow objects in these images to be mentioned in future story development.
@article{arxiv.2301.02777,
title = {Visual Story Generation Based on Emotion and Keywords},
author = {Yuetian Chen and Ruohua Li and Bowen Shi and Peiru Liu and Mei Si},
journal= {arXiv preprint arXiv:2301.02777},
year = {2023}
}