AI-generated content has attracted lots of attention recently, but photo-realistic video synthesis is still challenging. Although many attempts using GANs and autoregressive models have been made in this area, the visual quality and length of generated videos are far from satisfactory. Diffusion models (DMs) are another class of deep generative models and have recently achieved remarkable performance on various image synthesis tasks. However, training image diffusion models usually requires substantial computational resources to achieve a high performance, which makes expanding diffusion models to high-dimensional video synthesis tasks more computationally expensive. To ease this problem while leveraging its advantages, we introduce lightweight video diffusion models that synthesize high-fidelity and arbitrary-long videos from pure noise. Specifically, we propose to perform diffusion and denoising in a low-dimensional 3D latent space, which significantly outperforms previous methods on 3D pixel space when under a limited computational budget. In addition, though trained on tens of frames, our models can generate videos with arbitrary lengths, i.e., thousands of frames, in an autoregressive way. Finally, conditional latent perturbation is further introduced to reduce performance degradation during synthesizing long-duration videos. Extensive experiments on various datasets and generated lengths suggest that our framework is able to sample much more realistic and longer videos than previous approaches, including GAN-based, autoregressive-based, and diffusion-based methods.

该论文提出了一种基于轻量级视觉扩散模型和层级扩散概念的视频生成框架，可在有限的计算预算下生成更真实、更长的视频，同时提供了大规模文本到视频生成的扩展应用。

用于生成高保真长视频的潜在视频扩散模型