VChain: Chain-of-Visual-Thought for Reasoning in Video Generation
Ziqi Huang, Ning Yu, Gordon Chen, Haonan Qiu, Paul Debevec, Ziwei Liu
2025-10-07
Summary
This paper introduces a new method, VChain, to improve the quality of videos created by artificial intelligence, specifically focusing on making sure the events in the video make sense and follow a logical order.
What's the problem?
Current AI video generators are good at making videos *look* nice and smooth, but they often struggle with creating videos where things happen in a realistic and connected way. For example, if a video shows someone dropping a glass, the AI might not accurately show the glass breaking or the resulting mess. It's hard for these systems to understand how actions lead to consequences over time.
What's the solution?
The researchers combined the strengths of two types of AI: video generators and large multimodal models (like GPT-4o, which can understand both text and images). VChain works by using the multimodal model to predict important moments, or 'keyframes,' in the video. Then, it subtly adjusts the video generator *only* at those keyframes to ensure the events unfold logically. This approach is efficient because it doesn't require changing the video generator extensively or needing a lot of detailed instructions.
Why it matters?
This research is important because it addresses a major limitation of current AI video technology. By improving the logical consistency of generated videos, VChain brings us closer to creating AI that can produce realistic and believable visual stories, which has implications for entertainment, education, and many other fields.
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 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.