VChain: Chain-of-Visual-Thought for Reasoning in Video Generation
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.
68e47f85e4e093a7044e4d34
Ziqi Huang
claimed_verified
2025-10-07T12:27:00.662Z
60efe7fa0d920bc7805cada5
https://cdn-avatars.huggingface.co/v1/production/uploads/60efe7fa0d920bc7805cada5/2LBrJBjSCOP5ilZIpWLHl.png
Ziqi Huang
user
Ziqi
68e47f85e4e093a7044e4d35
Ning Yu
68e47f85e4e093a7044e4d36
Gordon Chen
claimed_verified
2025-10-07T12:26:58.557Z
65cf1549c1912289ca0b24cf
https://cdn-avatars.huggingface.co/v1/production/uploads/65cf1549c1912289ca0b24cf/kLCDspsG4HUjRTV6634nC.jpeg
Gordon Chen
user
gchen019
68e47f85e4e093a7044e4d37
Haonan Qiu
68e47f85e4e093a7044e4d38
Paul Debevec
68e47f85e4e093a7044e4d39
Ziwei Liu
VChain: Chain-of-Visual-Thought for Reasoning in Video Generation
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.
VChain enhances video generation by integrating visual reasoning from multimodal models to guide sparse tuning of a pre-trained video generator.
video generation visual reasoning multimodal models keyframes sparse inference-time tuning pre-trained video generator