A Pipeline for Story Visualization from Natural Language

Abstract

Generating automatic visualization from natural language texts is an important task for promoting language learning and literacy development for young children and language learners. However, translating a text into a coherent visualization matching its relevant keywords is a challenging problem. To tackle this issue, we proposed a robust story visualization pipeline ranging from NLP and relation extraction to image sequence generation and alignment. First, we applied a shallow semantic representation of the text where we extracted concepts including relevant characters, scene objects, and events in an appropriate format. We also distinguished between simple and complex actions. This distinction helped to realize an optimal visualization of the scene objects and their relationships according to the target audience. Second, we utilized an image generation framework along with different versions to support the visualization task efficiently. Third, we used CLIP similarity function as a semantic relevance metric to check local and global coherence to the whole story. Finally, we validated the scene sequence to compose a final visualization using the different versions for various target audiences. Our preliminary results showed considerable effectiveness in adopting such a pipeline for a coarse visualization task that can subsequently be enhanced.