VideoElevator: Elevating Video Generation Quality with Versatile
  Text-to-Image Diffusion Models
Text-to-image diffusion models (T2I) have demonstrated unprecedented
capabilities in creating realistic and aesthetic images. On the contrary,
text-to-video diffusion models (T2V) still lag far behind in frame quality and
text alignment, owing to insufficient quality and quantity of training videos.
In this paper, we introduce VideoElevator, a training-free and plug-and-play
method, which elevates the performance of T2V using superior capabilities of
T2I. Different from conventional T2V sampling (i.e., temporal and spatial
modeling), VideoElevator explicitly decomposes each sampling step into temporal
motion refining and spatial quality elevating. Specifically, temporal motion
refining uses encapsulated T2V to enhance temporal consistency, followed by
inverting to the noise distribution required by T2I. Then, spatial quality
elevating harnesses inflated T2I to directly predict less noisy latent, adding
more photo-realistic details. We have conducted experiments in extensive
prompts under the combination of various T2V and T2I. The results show that
VideoElevator not only improves the performance of T2V baselines with
foundational T2I, but also facilitates stylistic video synthesis with
personalized T2I. Our code is available at
https://github.com/YBYBZhang/VideoElevator.
65ee742339f0e647aeb9fbd7
Yabo Zhang
admin_assigned
2024-03-11T08:52:14.675Z
62cd752ba9be5c19555c2b4c
/avatars/ed72684f0f2139516ccde24cd467cea6.svg
YaboZhang
user
Yabo
65ee742339f0e647aeb9fbd8
Yuxiang Wei
admin_assigned
2024-03-11T08:52:35.641Z
6308bef17dc1b1a54cc9ff56
/avatars/d3793f6192a7441a575e27996c065030.svg
Yuxiang Wei
user
csyxwei
65ee742339f0e647aeb9fbd9
Xianhui Lin
admin_assigned
2024-03-11T08:52:48.508Z
6465e40a63e7e09dd02ef95d
/avatars/2b7f2c55eeca13a0c45e9fe2bd31df36.svg
lin
user
xianhui
65ee742339f0e647aeb9fbda
Zheng Hui
65ee742339f0e647aeb9fbdb
Peiran Ren
admin_assigned
2024-03-11T08:53:22.310Z
64b74a45f902508f0d786505
/avatars/8bc5aaa011642827e12524c4f0a56927.svg
Peiran REN
user
lyraestar
65ee742339f0e647aeb9fbdc
Xuansong Xie
65ee742339f0e647aeb9fbdd
Xiangyang Ji
65ee742339f0e647aeb9fbde
Wangmeng Zuo
VideoElevator: Elevating Video Generation Quality with Versatile
  Text-to-Image Diffusion Models
Text-to-image diffusion models (T2I) have demonstrated unprecedented
capabilities in creating realistic and aesthetic images. On the contrary,
text-to-video diffusion models (T2V) still lag far behind in frame quality and
text alignment, owing to insufficient quality and quantity of training videos.
In this paper, we introduce VideoElevator, a training-free and plug-and-play
method, which elevates the performance of T2V using superior capabilities of
T2I. Different from conventional T2V sampling (i.e., temporal and spatial
modeling), VideoElevator explicitly decomposes each sampling step into temporal
motion refining and spatial quality elevating. Specifically, temporal motion
refining uses encapsulated T2V to enhance temporal consistency, followed by
inverting to the noise distribution required by T2I. Then, spatial quality
elevating harnesses inflated T2I to directly predict less noisy latent, adding
more photo-realistic details. We have conducted experiments in extensive
prompts under the combination of various T2V and T2I. The results show that
VideoElevator not only improves the performance of T2V baselines with
foundational T2I, but also facilitates stylistic video synthesis with
personalized T2I. Our code is available at
https://github.com/YBYBZhang/VideoElevator.
VideoElevator enhances text-to-video diffusion models by decomposing sampling into temporal and spatial refinement, using capabilities of text-to-image diffusion models.
Text-to-image diffusion models text-to-video diffusion models VideoElevator temporal motion refining spatial quality elevating encapsulated T2V inflated T2I noise distribution photo-realistic details stylistic video synthesis