Mode Seeking meets Mean Seeking for Fast Long Video Generation

2026-02-27 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A new training paradigm, "Mode Seeking meets Mean Seeking," addresses the challenge of generating minute-scale videos by decoupling local fidelity from long-term coherence. This approach utilizes a Decoupled Diffusion Transformer with two heads: a global Flow Matching head, supervised on long videos to learn narrative structure, and a local Distribution Matching head. The local head aligns sliding windows to a frozen short-video teacher using a mode-seeking reverse-KL divergence, inheriting local realism. This strategy allows for the synthesis of minute-scale videos that learn long-range coherence and motion from limited long video data, while maintaining local realism from abundant short-video data. The method results in a few-step fast long video generator, effectively closing the fidelity-horizon gap by improving local sharpness, motion, and long-range consistency.

Key takeaway

For research scientists developing video generation models, this "Mode Seeking meets Mean Seeking" paradigm offers a robust solution to the fidelity-horizon gap. You should consider implementing a dual-head diffusion transformer architecture to leverage both limited long-form data for coherence and abundant short-form data for local realism, enabling faster and more consistent long video synthesis.

Key insights

Decoupling local fidelity and long-term coherence enables minute-scale video generation from limited long-form data.

Principles

• Combine supervised flow matching for global coherence.
• Align local segments to a frozen teacher for realism.

Method

A Decoupled Diffusion Transformer uses a global Flow Matching head for narrative structure and a local Distribution Matching head with mode-seeking reverse-KL divergence to align sliding windows to a short-video teacher.

In practice

• Generate minute-scale videos with high fidelity.
• Improve long-range consistency in video synthesis.

Topics

• Long Video Generation
• Diffusion Transformers
• Flow Matching
• Mode Seeking
• Video Synthesis

Best for: Computer Vision Engineer, Research Scientist, AI Researcher, AI Scientist, Deep Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.