Temporal Regularization Makes Your Video Generator Stronger
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.
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Harold Haodong Chen
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2025-03-20T10:45:09.768Z
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Harold Chen
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Harold328
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Haojian Huang
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2025-03-20T13:24:10.070Z
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Haojian Huang
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Jethro37
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Xianfeng Wu
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2025-03-20T13:24:16.210Z
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Xianfeng Wu
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Beckham808
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Yexin Liu
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2025-03-20T13:24:22.201Z
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Yexin Liu
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AI4VR
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Yajing Bai
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2025-03-20T13:24:28.817Z
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Yajing Bai
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YajingB
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Wen-Jie Shu
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Harry Yang
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Ser-Nam Lim
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2025-03-20T13:24:48.864Z
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Ser Nam Lim
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sernam
Temporal Regularization Makes Your Video Generator Stronger
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.
FluxFlow, a temporal augmentation strategy, enhances the temporal coherence and diversity in video generation without modifying model architectures.
temporal augmentation temporal perturbations spatial fidelity UCF-101 VBench U-Net DiT AR-based architectures