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Manga2Animate: An Agentic Framework for Faithful Manga Animation with Script and Storyboard Planning

Sheng-Wei Zhang, Hsi-An Chen, Yan-Ting Liou, Chih-Hsuan Chen, Shih-Chen Tseng, Yu-Lun Liu · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26851 · cited by 0 works (OpenAlex) · open-access PDF

German

Die Anpassung bestehender Manga in kohärente Animation, die der Quelle treu bleibt, ist eine wertvolle, aber herausfordernde Aufgabe. Bestehende Methoden entweder den Zugang zum Manga hauptsächlich durch Text, indem sie seine Charaktere und Kunststil verlieren, oder die Manga-Seiten direkt in Video-Generationsmodelle, die die Struktur des Manga falsch interpretieren und falsche Storyboards produzieren. In diesem Papier schlagen wir Manga2Animate vor, ein Multi-Agent-Framework, das jede Phase der Generation im Quellmanga begründet. Wir zerlegen die Aufgabe in zwei Zwischenrepräsentationen, ein Animations-Skript und ein visuelles Storyboard. Jede Darstellung wird von einem Schöpfer produziert und wird gegen die Quellpanels von

Machine translation (Meta M2M100); the English original follows.

Abstract

Adapting existing manga into coherent animation that stays faithful to the source is a valuable yet challenging task. Existing methods either access the manga mainly through text, losing its characters and art style, or feed manga pages directly into video generation models, which misinterpret the manga's structure and produce incorrect storylines. In this paper, we propose Manga2Animate, a multi-agent framework that grounds every stage of generation in the source manga. We decompose the task into two intermediate representations, an animation script and a visual storyboard. Each representation is produced by a creator agent and reviewed against the source panels by a paired reviewer agent. We tailor representation-specific selection and dual-level feedback to progressively refine both before synthesizing the final animation. Extensive experiments and a user study demonstrate that Manga2Animate produces animations more faithful to the source manga and more consistent across shots than state-of-the-art approaches. Project page: https://annchenn.github.io/Manga2Animate/

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Topics: Storyboard, Computer science, Animation, Selection (genetic algorithm), Representation (politics), Task (project management)

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