Diffusion Probabilistic Modeling for Video Generation. (arXiv:2203.09481v3 [cs.CV] UPDATED)

Denoising diffusion probabilistic models are a promising new class of
generative models that are competitive with GANs on perceptual metrics. In this
paper, we explore their potential for sequentially generating video. Inspired
by recent advances in neural video compression, we use denoising diffusion
models to stochastically generate a residual to a deterministic next-frame
prediction. We compare this approach to two sequential VAE and two GAN
baselines on four datasets, where we test the generated frames for perceptual
quality and forecasting …

arxiv cv modeling probabilistic modeling video