Photorealistic Video Generation with Diffusion Models
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.
6577f3b058d7a2cc89342628
Agrim Gupta
admin_assigned
2023-12-12T08:17:28.178Z
63defb9015266dd945f9f772
/avatars/b304ac6c3b45553358f8b6cd5c3df293.svg
Agrim
user
agrimgupta
6577f3b058d7a2cc89342629
Lijun Yu
admin_assigned
2023-12-12T08:17:49.551Z
639886ba65a34085ae29abb7
/avatars/51f4d4d6fbcc07ab8b7a4c1c2f7da53d.svg
Lijun Yu
user
lijunyu
6577f3b058d7a2cc8934262a
Kihyuk Sohn
6577f3b058d7a2cc8934262b
Xiuye Gu
6577f3b058d7a2cc8934262c
Meera Hahn
6577f3b058d7a2cc8934262d
Li Fei-Fei
6577f3b058d7a2cc8934262e
Irfan Essa
6577f3b058d7a2cc8934262f
Lu Jiang
claimed_verified
2023-12-23T11:18:10.788Z
64a5cba3bea0116f8f7187a7
/avatars/97b3bad82e359c57d1ab7d6e3aa7748b.svg
Lu Jiang
user
roadjiang
6577f3b058d7a2cc89342630
José Lezama
Photorealistic Video Generation with Diffusion Models
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.
causal encoder unified latent space window attention architecture joint spatial and spatiotemporal generative modeling Copeland (Copeland appears to be a typo; it seems like it should be Copeland or a reference to a specific model, but since it's not clear, it's excluded) transformer-based approach diffusion modeling video generation photorealistic video generation UCF-101 Kinetics-600 ImageNet state-of-the-art performance classifier-free guidance base latent video diffusion model video super-resolution diffusion models