VChain: Chain-of-Visual-Thought for Reasoning in Video Generation

2025-10-06

AI Summary

Existing video generation models struggle to model time-evolving causal relationships and state transitions in complex dynamic scenes, resulting in poor logical coherence. To address this, we propose VChain—a novel framework that, for the first time, injects the visual reasoning capabilities of multimodal large language models (MLLMs) into video generation via sparse keyframes, establishing a “visual chain-of-thought” mechanism. Specifically, MLLMs are invoked only at selected keyframes to infer intermediate states and causal constraints; these inferences then guide diffusion model generation through inference-time fine-tuning. Crucially, VChain requires no model retraining, ensuring low computational overhead and strong compatibility with existing architectures. Experiments demonstrate substantial improvements in both logical consistency and visual fidelity on multi-step causal scenarios—such as object interaction and physical evolution—validating the fundamental benefit of explicitly incorporating visual causal reasoning into video generation.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Addresses incoherent chain-of-consequences in video generation
• Models complex visual outcomes and state transitions over time
• Enhances video quality through sparse keyframe-guided reasoning

Innovation

• Uses multimodal models to generate keyframes
• Guides sparse inference-time tuning of video generator
• Enhances video quality with minimal overhead