VChain: Chain-of-Visual-Thought for Reasoning in Video Generation
Authors: name: Ziqi Huang, name: Ning Yu, name: Gordon Chen, name: Haonan Qiu, name: Paul Debevec, name: Ziwei Liu

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 tuning 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.