title: Long Context Tuning for Video Generation
summary: Recent advances in video generation can produce realistic, minute-long
  single-shot videos with scalable diffusion transformers. However, real-world
  narrative videos require multi-shot scenes with visual and dynamic consistency
  across shots. In this work, we introduce Long Context Tuning (LCT), a training
  paradigm that expands the context window of pre-trained single-shot video
  diffusion models to learn scene-level consistency directly from data. Our
  method expands full attention mechanisms from individual shots to encompass all
  shots within a scene, incorporating interleaved 3D position embedding and an
  asynchronous noise strategy, enabling both joint and auto-regressive shot
  generation without additional parameters. Models with bidirectional attention
  after LCT can further be fine-tuned with context-causal attention, facilitating
  auto-regressive generation with efficient KV-cache. Experiments demonstrate
  single-shot models after LCT can produce coherent multi-shot scenes and exhibit
  emerging capabilities, including compositional generation and interactive shot
  extension, paving the way for more practical visual content creation. See
  https://guoyww.github.io/projects/long-context-video/ for more details.
paper_authors._id: 67d3adc5c14480a46038cecf
paper_authors.name: Yuwei Guo
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paper_authors.fullname: Yuwei Guo
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paper_authors.name: Ceyuan Yang
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paper_authors.name: Ziyan Yang
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paper_authors.fullname: Ziyan Yang
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paper_authors.name: Zhibei Ma
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paper_authors.fullname: Zhibei Ma
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paper_authors.user: Brightmzb
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paper_authors.name: Zhijie Lin
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paper_authors.fullname: Zhijie Lin
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paper_authors.user: Ikuinen
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paper_authors.name: Zhenheng Yang
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paper_authors.fullname: Zhenheng Yang
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paper_authors.user: zhenheny
paper_authors._id: 67d3adc5c14480a46038ced5
paper_authors.name: Dahua Lin
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paper_authors._id: 636317ed80c1a705a6eff396
paper_authors.avatarUrl: /avatars/3db090e101b916d9256d0d3e043db71d.svg
paper_authors.fullname: Dahua Lin
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paper_authors.user: lindahua
paper_authors._id: 67d3adc5c14480a46038ced6
paper_authors.name: Lu Jiang
paper_title: Long Context Tuning for Video Generation
paper_summary: Recent advances in video generation can produce realistic, minute-long
  single-shot videos with scalable diffusion transformers. However, real-world
  narrative videos require multi-shot scenes with visual and dynamic consistency
  across shots. In this work, we introduce Long Context Tuning (LCT), a training
  paradigm that expands the context window of pre-trained single-shot video
  diffusion models to learn scene-level consistency directly from data. Our
  method expands full attention mechanisms from individual shots to encompass all
  shots within a scene, incorporating interleaved 3D position embedding and an
  asynchronous noise strategy, enabling both joint and auto-regressive shot
  generation without additional parameters. Models with bidirectional attention
  after LCT can further be fine-tuned with context-causal attention, facilitating
  auto-regressive generation with efficient KV-cache. Experiments demonstrate
  single-shot models after LCT can produce coherent multi-shot scenes and exhibit
  emerging capabilities, including compositional generation and interactive shot
  extension, paving the way for more practical visual content creation. See
  https://guoyww.github.io/projects/long-context-video/ for more details.
paper_ai_keywords: scalable diffusion transformers context window scene-level consistency Long Context Tuning (LCT) full attention mechanisms interleaved 3D position embedding asynchronous noise strategy joint and auto-regressive shot generation bidirectional attention context-causal attention efficient KV-cache compositional generation interactive shot extension