Character-Centric Storytelling
Aditya Surikuchi, Jorma Laaksonen

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.
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Taxonomy
TopicsDigital Storytelling and Education · Artificial Intelligence in Games · Multimodal Machine Learning Applications
