Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Diffusion Probabilistic Modeling for Video Generation   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 accuracy against ground truth frames. We find
significant improvements in terms of perceptual quality on all data and
improvements in terms of frame forecasting for complex high-resolution videos.

28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
3
The categories of this paper are: physics