Photorealistic Video Generation with Diffusion Models

• URL: http://arxiv.org/abs/2312.06662v1
• Date: Mon, 11 Dec 2023 18:59:57 GMT
• Title: Photorealistic Video Generation with Diffusion Models
• Authors: Agrim Gupta, Lijun Yu, Kihyuk Sohn, Xiuye Gu, Meera Hahn, Li Fei-Fei, Irfan Essa, Lu Jiang, Jos\'e Lezama
• Abstract summary: W.A.L.T. is a transformer-based approach for video generation via diffusion modeling. We use a causal encoder to jointly compress images and videos within a unified latent space, enabling training and generation across modalities. We also train a cascade of three models for the task of text-to-video generation consisting of a base latent video diffusion model, and two video super-resolution diffusion models to generate videos of $512 times $ resolution at $8$ frames per second.
• Score: 44.95407324724976
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present W.A.L.T, a transformer-based approach for photorealistic video generation via diffusion modeling. Our approach has two key design decisions. First, we use a causal encoder to jointly compress images and videos within a unified latent space, enabling training and generation across modalities. Second, for memory and training efficiency, we use a window attention architecture tailored for joint spatial and spatiotemporal generative modeling. Taken together these design decisions enable us to achieve state-of-the-art performance on established video (UCF-101 and Kinetics-600) and image (ImageNet) generation benchmarks without using classifier free guidance. Finally, we also train a cascade of three models for the task of text-to-video generation consisting of a base latent video diffusion model, and two video super-resolution diffusion models to generate videos of $512 \times 896$ resolution at $8$ frames per second.

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