  Recognizing characters and predicting speakers of dialogue are critical for
comic processing tasks, such as voice generation or translation. However,
because characters vary by comic title, supervised learning approaches like
training character classifiers which require specific annotations for each
comic title are infeasible. This motivates us to propose a novel zero-shot
approach, allowing machines to identify characters and predict speaker names
based solely on unannotated comic images. In spite of their importance in
real-world applications, these task have largely remained unexplored due to
challenges in story comprehension and multimodal integration. Recent large
language models (LLMs) have shown great capability for text understanding and
reasoning, while their application to multimodal content analysis is still an
open problem. To address this problem, we propose an iterative multimodal
framework, the first to employ multimodal information for both character
identification and speaker prediction tasks. Our experiments demonstrate the
effectiveness of the proposed framework, establishing a robust baseline for
these tasks. Furthermore, since our method requires no training data or
annotations, it can be used as-is on any comic series.
