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    <abstract>Recent video generation models can produce smooth and visually appealing clips, but they often struggle to synthesize complex dynamics with a coherent chain of consequences. Accurately modeling visual outcomes and state transitions over time remains a core challenge. In contrast, large language and multimodal models (e.g., GPT-4o) exhibit strong visual state reasoning and future prediction capabilities. To bridge these strengths, we introduce VChain, a novel inference-time chain-of-visual-thought framework that injects visual reasoning signals from multimodal models into video generation. Specifically, VChain contains a dedicated pipeline that leverages large multimodal models to generate a sparse set of critical keyframes as snapshots, which are then used to guide the sparse inference-time visual-state adaptation of a pre-trained video generator only at these key moments. Our approach is tuning-efficient, introduces minimal overhead and avoids dense supervision. Extensive experiments on complex, multi-step scenarios show that VChain significantly enhances the quality of generated videos.</abstract>
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%0 Conference Proceedings
%T VChain: Chain-of-Visual-Thought for Reasoning in Video Generation
%A Huang, Ziqi
%A Yu, Ning
%A Chen, Gordon
%A Qiu, Haonan
%A Debevec, Paul
%A Liu, Ziwei
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F huang-etal-2026-vchain
%X Recent video generation models can produce smooth and visually appealing clips, but they often struggle to synthesize complex dynamics with a coherent chain of consequences. Accurately modeling visual outcomes and state transitions over time remains a core challenge. In contrast, large language and multimodal models (e.g., GPT-4o) exhibit strong visual state reasoning and future prediction capabilities. To bridge these strengths, we introduce VChain, a novel inference-time chain-of-visual-thought framework that injects visual reasoning signals from multimodal models into video generation. Specifically, VChain contains a dedicated pipeline that leverages large multimodal models to generate a sparse set of critical keyframes as snapshots, which are then used to guide the sparse inference-time visual-state adaptation of a pre-trained video generator only at these key moments. Our approach is tuning-efficient, introduces minimal overhead and avoids dense supervision. Extensive experiments on complex, multi-step scenarios show that VChain significantly enhances the quality of generated videos.
%R 10.18653/v1/2026.findings-acl.12
%U https://aclanthology.org/2026.findings-acl.12/
%U https://doi.org/10.18653/v1/2026.findings-acl.12
%P 226-250

Markdown (Informal)

[VChain: Chain-of-Visual-Thought for Reasoning in Video Generation](https://aclanthology.org/2026.findings-acl.12/) (Huang et al., Findings 2026)

• VChain: Chain-of-Visual-Thought for Reasoning in Video Generation (Huang et al., Findings 2026)

ACL

• Ziqi Huang, Ning Yu, Gordon Chen, Haonan Qiu, Paul Debevec, and Ziwei Liu. 2026. VChain: Chain-of-Visual-Thought for Reasoning in Video Generation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 226–250, San Diego, California, United States. Association for Computational Linguistics.