title: Temporal Regularization Makes Your Video Generator Stronger
summary: Temporal quality is a critical aspect of video generation, as it ensures
  consistent motion and realistic dynamics across frames. However, achieving high
  temporal coherence and diversity remains challenging. In this work, we explore
  temporal augmentation in video generation for the first time, and introduce
  FluxFlow for initial investigation, a strategy designed to enhance temporal
  quality. Operating at the data level, FluxFlow applies controlled temporal
  perturbations without requiring architectural modifications. Extensive
  experiments on UCF-101 and VBench benchmarks demonstrate that FluxFlow
  significantly improves temporal coherence and diversity across various video
  generation models, including U-Net, DiT, and AR-based architectures, while
  preserving spatial fidelity. These findings highlight the potential of temporal
  augmentation as a simple yet effective approach to advancing video generation
  quality.
paper_authors._id: 67db8e05842d8b6642a135d0
paper_authors.name: Harold Haodong Chen
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paper_authors.fullname: Harold Chen
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paper_authors.name: Haojian Huang
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paper_authors.fullname: Haojian Huang
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paper_authors.user: Jethro37
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paper_authors.name: Xianfeng Wu
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paper_authors.fullname: Xianfeng Wu
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paper_authors.user: Beckham808
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paper_authors.name: Yexin Liu
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paper_authors.fullname: Yexin Liu
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paper_authors.user: AI4VR
paper_authors._id: 67db8e05842d8b6642a135d4
paper_authors.name: Yajing Bai
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paper_authors.fullname: Yajing Bai
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paper_authors.user: YajingB
paper_authors._id: 67db8e05842d8b6642a135d5
paper_authors.name: Wen-Jie Shu
paper_authors._id: 67db8e05842d8b6642a135d6
paper_authors.name: Harry Yang
paper_authors._id: 67db8e05842d8b6642a135d7
paper_authors.name: Ser-Nam Lim
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-20T13:24:48.864Z
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paper_authors.fullname: Ser Nam Lim
paper_authors.type: user
paper_authors.user: sernam
paper_title: Temporal Regularization Makes Your Video Generator Stronger
paper_summary: Temporal quality is a critical aspect of video generation, as it ensures
  consistent motion and realistic dynamics across frames. However, achieving high
  temporal coherence and diversity remains challenging. In this work, we explore
  temporal augmentation in video generation for the first time, and introduce
  FluxFlow for initial investigation, a strategy designed to enhance temporal
  quality. Operating at the data level, FluxFlow applies controlled temporal
  perturbations without requiring architectural modifications. Extensive
  experiments on UCF-101 and VBench benchmarks demonstrate that FluxFlow
  significantly improves temporal coherence and diversity across various video
  generation models, including U-Net, DiT, and AR-based architectures, while
  preserving spatial fidelity. These findings highlight the potential of temporal
  augmentation as a simple yet effective approach to advancing video generation
  quality.
paper_ai_summary: FluxFlow, a temporal augmentation strategy, enhances the temporal coherence and diversity in video generation without modifying model architectures.
paper_ai_keywords: temporal augmentation temporal perturbations spatial fidelity UCF-101 VBench U-Net DiT AR-based architectures