Character-Centric Storytelling

TL;DR

This paper introduces a character-centric storytelling model that explicitly learns relationships between characters in image sequences to generate more comprehensive and inclusive narratives.

Contribution

It presents a novel model that implicitly learns character relationships, addressing gaps in existing visual storytelling methods.

Findings

Model effectively captures character relationships

Generated stories include all relevant characters

Provides detailed dataset statistics

Abstract

Sequential vision-to-language or visual storytelling has recently been one of the areas of focus in computer vision and language modeling domains. Though existing models generate narratives that read subjectively well, there could be cases when these models miss out on generating stories that account and address all prospective human and animal characters in the image sequences. Considering this scenario, we propose a model that implicitly learns relationships between provided characters and thereby generates stories with respective characters in scope. We use the VIST dataset for this purpose and report numerous statistics on the dataset. Eventually, we describe the model, explain the experiment and discuss our current status and future work.