Note: If some videos are not displayed properly, try out refreshing the page!
Despite the remarkable progress in deep generative models, synthesizing high-resolution and temporally coherent videos still remains a challenge due to their high-dimensionality and complex temporal dynamics along with large spatial variations. Recent works on diffusion models have shown their potential to solve this challenge, yet they suffer from severe computation- and memory-inefficiency that limit the scalability. To handle this issue, we propose a novel generative model for videos, coined projected latent video diffusion models (PVDM), a probabilistic diffusion model which learns a video distribution in a low-dimensional latent space and thus can be efficiently trained with high-resolution videos under limited resources. Specifically, PVDM is composed of two components: (a) an autoencoder that projects a given video as 2D-shaped latent vectors that factorize the complex cubic structure of video pixels and (b) a diffusion model architecture specialized for our new factorized latent space and the training/sampling procedure to synthesize videos of arbitrary length with a single model. Experiments on popular video generation datasets demonstrate the superiority of PVDM compared with previous video synthesis methods; e.g., PVDM obtains the FVD score of 639.7 on the UCF-101 long video (128 frames) generation benchmark, which improves 1773.4 of the prior state-of-the-art.
Note: We provide both short and long videos from our method; note that short videos of baselines correspond to the consecutively subsampled 16 frames in long video examples! For a fair comparison, we use qualitative results of baselines (DIGAN and StyleGAN-V) from the official StyleGAN-V webpage.
SY thanks Jaehyung Kim, Jihoon Tack, and Younggyo Seo for their helpful discussions and comments.
SY also acknowledges Ivan Skorokhodov for providing checkpoints and qualitative results of StyleGAN-V and other baselines.
This template was originally made by Subin Kim and Sihyun Yu for a NVP project.