  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.
