A Mechanistic View on Video Generation as World Models: State and Dynamics
Large-scale video generation models have demonstrated emergent physical coherence, positioning them as potential world models. However, a gap remains between contemporary "stateless" video architectures and classic state-centric world model theories. This work bridges this gap by proposing a novel taxonomy centered on two pillars: State Construction and Dynamics Modeling. We categorize state construction into implicit paradigms (context management) and explicit paradigms (latent compression), while dynamics modeling is analyzed through knowledge integration and architectural reformulation. Furthermore, we advocate for a transition in evaluation from visual fidelity to functional benchmarks, testing physical persistence and causal reasoning. We conclude by identifying two critical frontiers: enhancing persistence via data-driven memory and compressed fidelity, and advancing causality through latent factor decoupling and reasoning-prior integration. By addressing these challenges, the field can evolve from generating visually plausible videos to building robust, general-purpose world simulators.
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Luozhou Wang
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2026-01-27T14:42:47.993Z
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Zhifei Chen
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ZhiFei Chen
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Yihua Du
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Yihua Du
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Dongyu Yan
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Dongyu Yan
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Wenhang Ge
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Guibao Shen
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Guibao SHEN
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Xinli Xu
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Xinli XU
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Leyi Wu
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Man Chen
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Tianshuo Xu
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Tianshuo Xu
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Peiran Ren
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Peiran REN
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Xin Tao
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Pengfei Wan
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Ying-Cong Chen
A Mechanistic View on Video Generation as World Models: State and Dynamics
Large-scale video generation models have demonstrated emergent physical coherence, positioning them as potential world models. However, a gap remains between contemporary "stateless" video architectures and classic state-centric world model theories. This work bridges this gap by proposing a novel taxonomy centered on two pillars: State Construction and Dynamics Modeling. We categorize state construction into implicit paradigms (context management) and explicit paradigms (latent compression), while dynamics modeling is analyzed through knowledge integration and architectural reformulation. Furthermore, we advocate for a transition in evaluation from visual fidelity to functional benchmarks, testing physical persistence and causal reasoning. We conclude by identifying two critical frontiers: enhancing persistence via data-driven memory and compressed fidelity, and advancing causality through latent factor decoupling and reasoning-prior integration. By addressing these challenges, the field can evolve from generating visually plausible videos to building robust, general-purpose world simulators.
Video generation models are categorized based on state construction and dynamics modeling approaches, with emphasis on transitioning evaluation metrics from visual quality to functional capabilities like physical persistence and causal reasoning.
video generation models world models state construction dynamics modeling implicit paradigms explicit paradigms context management latent compression knowledge integration architectural reformulation physical persistence causal reasoning data-driven memory compressed fidelity latent factor decoupling reasoning-prior integration