Mode Seeking meets Mean Seeking for Fast Long Video Generation
Scaling video generation from seconds to minutes faces a critical bottleneck: while short-video data is abundant and high-fidelity, coherent long-form data is scarce and limited to narrow domains. To address this, we propose a training paradigm where Mode Seeking meets Mean Seeking, decoupling local fidelity from long-term coherence based on a unified representation via a Decoupled Diffusion Transformer. Our approach utilizes a global Flow Matching head trained via supervised learning on long videos to capture narrative structure, while simultaneously employing a local Distribution Matching head that aligns sliding windows to a frozen short-video teacher via a mode-seeking reverse-KL divergence. This strategy enables the synthesis of minute-scale videos that learns long-range coherence and motions from limited long videos via supervised flow matching, while inheriting local realism by aligning every sliding-window segment of the student to a frozen short-video teacher, resulting in a few-step fast long video generator. Evaluations show that our method effectively closes the fidelity-horizon gap by jointly improving local sharpness, motion and long-range consistency. Project website: https://primecai.github.io/mmm/.
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Shengqu Cai
claimed_verified
2026-03-02T09:39:47.744Z
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Shengqu Cai
primecai
user
primecai
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Weili Nie
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Chao Liu
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Julius Berner
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2026-03-02T09:37:23.633Z
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Julius Berner
julberner
user
julberner
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Lvmin Zhang
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Nanye Ma
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Hansheng Chen
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Maneesh Agrawala
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Leonidas Guibas
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Gordon Wetzstein
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Arash Vahdat
Mode Seeking meets Mean Seeking for Fast Long Video Generation
Scaling video generation from seconds to minutes faces a critical bottleneck: while short-video data is abundant and high-fidelity, coherent long-form data is scarce and limited to narrow domains. To address this, we propose a training paradigm where Mode Seeking meets Mean Seeking, decoupling local fidelity from long-term coherence based on a unified representation via a Decoupled Diffusion Transformer. Our approach utilizes a global Flow Matching head trained via supervised learning on long videos to capture narrative structure, while simultaneously employing a local Distribution Matching head that aligns sliding windows to a frozen short-video teacher via a mode-seeking reverse-KL divergence. This strategy enables the synthesis of minute-scale videos that learns long-range coherence and motions from limited long videos via supervised flow matching, while inheriting local realism by aligning every sliding-window segment of the student to a frozen short-video teacher, resulting in a few-step fast long video generator. Evaluations show that our method effectively closes the fidelity-horizon gap by jointly improving local sharpness, motion and long-range consistency. Project website: https://primecai.github.io/mmm/.
A training paradigm combining mode seeking and mean seeking in a Decoupled Diffusion Transformer enables efficient generation of high-quality long videos by leveraging both global flow matching and local distribution matching techniques.
Decoupled Diffusion Transformer flow matching distribution matching mode-seeking reverse-KL divergence long-range coherence local fidelity global Flow Matching head local Distribution Matching head sliding window segments short-video teacher