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    <abstract>End-to-end manga generation is a structured visual storytelling task that requires story decomposition, recurring character and scene grounding, page layout design, panel rendering, page composition, and lettering. However, existing generative models often perform direct page synthesis, entangling these factors in a single visual output and limiting precise control over layout geometry, visual references, and cross-panel consistency. To address these limitations, we propose MangaFlow, an agentic framework for controllable long-form manga generation that decomposes manga creation into planning, grounding, layout construction, reference-conditioned rendering, composition, and text placement. By treating layout and visual references as explicit intermediate variables, MangaFlow enables both simple text-to-manga generation and more precise user-controlled manga creation. This design exposes layout, visual assets, and lettering as editable intermediate controls for refining panel geometry, references, and text placement. To support long-form consistency, MangaFlow introduces a story section memory that links section descriptions with corresponding character, scene, and object references for reuse across panels. We further present a meta-benchmark for evaluating layout controllability, visual consistency, and generation quality. Experiments show that MangaFlow improves layout adherence and cross-panel consistency over direct generation baselines while supporting flexible human control.</abstract>
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%0 Conference Proceedings
%T MangaFlow: An End-to-End Agentic Framework for Controllable Story to Manga Generation
%A Wang, Muyao
%A Xie, Zeke
%A Chen, Yanhao
%A Xiu, Lixin
%A Nakayama, Hideki
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wang-etal-2026-mangaflow
%X End-to-end manga generation is a structured visual storytelling task that requires story decomposition, recurring character and scene grounding, page layout design, panel rendering, page composition, and lettering. However, existing generative models often perform direct page synthesis, entangling these factors in a single visual output and limiting precise control over layout geometry, visual references, and cross-panel consistency. To address these limitations, we propose MangaFlow, an agentic framework for controllable long-form manga generation that decomposes manga creation into planning, grounding, layout construction, reference-conditioned rendering, composition, and text placement. By treating layout and visual references as explicit intermediate variables, MangaFlow enables both simple text-to-manga generation and more precise user-controlled manga creation. This design exposes layout, visual assets, and lettering as editable intermediate controls for refining panel geometry, references, and text placement. To support long-form consistency, MangaFlow introduces a story section memory that links section descriptions with corresponding character, scene, and object references for reuse across panels. We further present a meta-benchmark for evaluating layout controllability, visual consistency, and generation quality. Experiments show that MangaFlow improves layout adherence and cross-panel consistency over direct generation baselines while supporting flexible human control.
%U https://aclanthology.org/2026.emnlp-main.263/
%P 5985-6002

Markdown (Informal)

[MangaFlow: An End-to-End Agentic Framework for Controllable Story to Manga Generation](https://aclanthology.org/2026.emnlp-main.263/) (Wang et al., EMNLP 2026)

• MangaFlow: An End-to-End Agentic Framework for Controllable Story to Manga Generation (Wang et al., EMNLP 2026)

ACL

• Muyao Wang, Zeke Xie, Yanhao Chen, Lixin Xiu, and Hideki Nakayama. 2026. MangaFlow: An End-to-End Agentic Framework for Controllable Story to Manga Generation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 5985–6002, Budapest, Hungary. Association for Computational Linguistics.