Denoising diffusion probabilistic models are a promising new class of generative models that mark a milestone in high-quality image generation. This paper showcases their ability to sequentially generate video, surpassing prior methods in perceptual and probabilistic forecasting metrics. We propose an autoregressive, end-to-end optimized video diffusion model inspired by recent advances in neural video compression. The model successively generates future frames by correcting a deterministic next-frame prediction using a stochastic residual generated by an inverse diffusion process. We compare this approach against five baselines on four datasets involving natural and simulation-based videos. We find significant improvements in terms of perceptual quality for all datasets. Furthermore, by introducing a scalable version of the Continuous Ranked Probability Score (CRPS) applicable to video, we show that our model also outperforms existing approaches in their probabilistic frame forecasting ability.

翻译：拒绝扩散的概率模型是一种有希望的新基因模型,是高质量图像生成过程中的一个里程碑。本文展示了它们连续制作视频的能力,超过了先前在感知性和概率性预测指标方面采用的方法。我们提出了一个由神经视频压缩方面最近进展所启发的自动递减、端到端优化的视频传播模型。该模型通过利用反向传播过程产生的随机残渣纠正确定性的下框架预测,连续生成未来框架。我们比较了这一方法与涉及自然和模拟视频的四个数据集的5个基线。我们发现所有数据集的感知质量有了显著改进。此外,通过引入一个适用于视频的可缩放版连续分级概率分数(CRPS),我们展示了我们的模型还优于其预测能力的现有方法。