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Diffusion Probabilistic Modeling for Video Generation

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arxiv 2203.09481 v5 pith:UM243XLP submitted 2022-03-16 cs.CV cs.LGstat.ML

Ruihan Yang , Prakhar Srivastava , Stephan Mandt This is my paper
classification cs.CVcs.LGstat.ML
keywords videodiffusionprobabilisticmodelabilitydatasetsforecastinggeneration
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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