@inproceedings{huang-etal-2026-dramaagent,
title = "{D}rama{A}gent: Agentic Storytelling Video Generation",
author = "Huang, Ting and
Wu, Biao and
Chen, Ronghao and
Zhang, Zeyu and
Cheng, Tengfei and
Lan, Qizhen and
Wang, Huacan and
Tang, Hao",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.163/",
pages = "3609--3624",
ISBN = "979-8-89176-439-2",
abstract = "Recent diffusion and autoregressive video generators have substantially improved short-form synthesis, yet composing multiple independently generated clips into a coherent audiovisual story remains challenging. Character identities may drift across scenes, narrative events can become inconsistent, and dialogue or sound may not align with the evolving visual context. We present **DramaAgent**, a hierarchical, agentic, and model-agnostic framework for multi-scene long-horizon audiovisual story generation. Rather than modifying the underlying video backbone, DramaAgent introduces a stateful control layer that combines hierarchical story planning, persistent character conditioning, scene-wise generation, and reflection-guided targeted repair. The framework maintains reusable story and character states across scenes, diagnoses failures along identity, semantic, temporal, and audio-visual dimensions, and maps each diagnosed failure to a corresponding localized repair operation. Experiments on 50 narrative scripts across five video-generation backbones show consistent improvements in semantic alignment, character consistency, narrative fidelity, and scene-level audio-visual compatibility. Controlled same-backbone comparisons further show that these gains cannot be explained solely by stronger generators or additional sampling. Our results suggest that persistent state, failure diagnosis, and targeted repair provide a practical control paradigm for coherent multi-scene audiovisual generation."
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<abstract>Recent diffusion and autoregressive video generators have substantially improved short-form synthesis, yet composing multiple independently generated clips into a coherent audiovisual story remains challenging. Character identities may drift across scenes, narrative events can become inconsistent, and dialogue or sound may not align with the evolving visual context. We present **DramaAgent**, a hierarchical, agentic, and model-agnostic framework for multi-scene long-horizon audiovisual story generation. Rather than modifying the underlying video backbone, DramaAgent introduces a stateful control layer that combines hierarchical story planning, persistent character conditioning, scene-wise generation, and reflection-guided targeted repair. The framework maintains reusable story and character states across scenes, diagnoses failures along identity, semantic, temporal, and audio-visual dimensions, and maps each diagnosed failure to a corresponding localized repair operation. Experiments on 50 narrative scripts across five video-generation backbones show consistent improvements in semantic alignment, character consistency, narrative fidelity, and scene-level audio-visual compatibility. Controlled same-backbone comparisons further show that these gains cannot be explained solely by stronger generators or additional sampling. Our results suggest that persistent state, failure diagnosis, and targeted repair provide a practical control paradigm for coherent multi-scene audiovisual generation.</abstract>
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%0 Conference Proceedings
%T DramaAgent: Agentic Storytelling Video Generation
%A Huang, Ting
%A Wu, Biao
%A Chen, Ronghao
%A Zhang, Zeyu
%A Cheng, Tengfei
%A Lan, Qizhen
%A Wang, Huacan
%A Tang, Hao
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F huang-etal-2026-dramaagent
%X Recent diffusion and autoregressive video generators have substantially improved short-form synthesis, yet composing multiple independently generated clips into a coherent audiovisual story remains challenging. Character identities may drift across scenes, narrative events can become inconsistent, and dialogue or sound may not align with the evolving visual context. We present **DramaAgent**, a hierarchical, agentic, and model-agnostic framework for multi-scene long-horizon audiovisual story generation. Rather than modifying the underlying video backbone, DramaAgent introduces a stateful control layer that combines hierarchical story planning, persistent character conditioning, scene-wise generation, and reflection-guided targeted repair. The framework maintains reusable story and character states across scenes, diagnoses failures along identity, semantic, temporal, and audio-visual dimensions, and maps each diagnosed failure to a corresponding localized repair operation. Experiments on 50 narrative scripts across five video-generation backbones show consistent improvements in semantic alignment, character consistency, narrative fidelity, and scene-level audio-visual compatibility. Controlled same-backbone comparisons further show that these gains cannot be explained solely by stronger generators or additional sampling. Our results suggest that persistent state, failure diagnosis, and targeted repair provide a practical control paradigm for coherent multi-scene audiovisual generation.
%U https://aclanthology.org/2026.aacl-long.163/
%P 3609-3624
Markdown (Informal)
[DramaAgent: Agentic Storytelling Video Generation](https://aclanthology.org/2026.aacl-long.163/) (Huang et al., AACL-IJCNLP 2026)
ACL
- Ting Huang, Biao Wu, Ronghao Chen, Zeyu Zhang, Tengfei Cheng, Qizhen Lan, Huacan Wang, and Hao Tang. 2026. DramaAgent: Agentic Storytelling Video Generation. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3609–3624, Hengqin, China. Association for Computational Linguistics.