Temporal Regularization Makes Your Video Generator Stronger
Authors: name: Harold Haodong Chen, name: Haojian Huang, name: Xianfeng Wu, name: Yexin Liu, name: Yajing Bai, name: Wen-Jie Shu, name: Harry Yang, name: Ser-Nam Lim

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.