Papers › VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation
VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation
Zhengxiong Luo, Dayou Chen, Yingya Zhang, Yan Huang, Liang Wang, Yujun Shen, Deli Zhao, Jingren Zhou, Tieniu Tan
A diffusion probabilistic model (DPM), which constructs a forward diffusion process by gradually adding noise to data points and learns the reverse denoising process to generate new samples, has been shown to handle complex data distribution. Despite its recent success in image synthesis, applying DPMs to video generation is still challenging due to high-dimensional data spaces. Previous methods usually adopt a standard diffusion process, where frames in the same video clip are destroyed with independent noises, ignoring the content redundancy and temporal correlation. This work presents a decomposed diffusion process via resolving the per-frame noise into a base noise that is shared among all frames and a residual noise that varies along the time axis. The denoising pipeline employs two jointly-learned networks to match the noise decomposition accordingly. Experiments on various datasets confirm that our approach, termed as VideoFusion, surpasses both GAN-based and diffusion-based alternatives in high-quality video generation. We further show that our decomposed formulation can benefit from pre-trained image diffusion models and well-support text-conditioned video creation.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video Generation | UCF-101 | VideoFusion (128x128, class-conditional) | FVD16 | 173 | #14 of 48 | Archive leaderboard | report |
| Video Generation | UCF-101 | VideoFusion (128x128, class-conditional) | Inception Score | 80.03 | #14 of 48 | Archive leaderboard | report |
| Video Generation | UCF-101 | VideoFusion (128x128, unconditional) | FVD16 | 220 | #16 of 48 | Archive leaderboard | report |
| Video Generation | UCF-101 | VideoFusion (128x128, unconditional) | Inception Score | 72.22 | #16 of 48 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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