Generative AI has established the opportunity to readily transform content from one medium to another. This capability is especially powerful for storytelling, where visual illustrations can illuminate a story originally expressed in text. In this paper, we focus on the task of narrative scene illustration, which involves automatically generating an image depicting a scene in a story. Motivated by recent progress on text-to-image models, we consider a pipeline that uses LLMs as an interface for prompting text-to-image models to generate scene illustrations given raw story text. We apply variations of this pipeline to a prominent story corpus in order to synthesize illustrations for scenes in these stories. We conduct a human annotation task to obtain pairwise quality judgments for these illustrations. The outcome of this process is the SceneIllustrations dataset, which we release as a new resource for future work on cross-modal narrative transformation. Through our analysis of this dataset and experiments modeling illustration quality, we demonstrate that LLMs can effectively verbalize scene knowledge implicitly evoked by story text. Moreover, this capability is impactful for generating and evaluating illustrations.

Improvements in language technology have led to an increasing interest in writing support tools. In this paper we propose a design space for such tools based on a cognitive process model of writing. We conduct a systematic review of recent computer science papers that present and/or study such tools, analyzing 30 papers from the last five years using the design space. Tools are plotted according to three distinct cognitive processes–planning, translating, and reviewing–and the level of constraint each process entails. Analyzing recent work with the design space shows that highly constrained planning and reviewing are under-studied areas that recent technology improvements may now be able to serve. Finally, we propose shared evaluation methodologies and tasks that may help the field mature.