VideoCrafter2: Overcoming Data Limitations for High-Quality Video
  Diffusion Models
Text-to-video generation aims to produce a video based on a given prompt.
Recently, several commercial video models have been able to generate plausible
videos with minimal noise, excellent details, and high aesthetic scores.
However, these models rely on large-scale, well-filtered, high-quality videos
that are not accessible to the community. Many existing research works, which
train models using the low-quality WebVid-10M dataset, struggle to generate
high-quality videos because the models are optimized to fit WebVid-10M. In this
work, we explore the training scheme of video models extended from Stable
Diffusion and investigate the feasibility of leveraging low-quality videos and
synthesized high-quality images to obtain a high-quality video model. We first
analyze the connection between the spatial and temporal modules of video models
and the distribution shift to low-quality videos. We observe that full training
of all modules results in a stronger coupling between spatial and temporal
modules than only training temporal modules. Based on this stronger coupling,
we shift the distribution to higher quality without motion degradation by
finetuning spatial modules with high-quality images, resulting in a generic
high-quality video model. Evaluations are conducted to demonstrate the
superiority of the proposed method, particularly in picture quality, motion,
and concept composition.
65a8938c2984fa7203d6cf93
Haoxin Chen
claimed_verified
2024-01-19T09:23:13.146Z
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Haoxin Chen
user
paulai
65a8938c2984fa7203d6cf94
Yong Zhang
65a8938c2984fa7203d6cf95
Xiaodong Cun
admin_assigned
2024-01-18T10:10:17.994Z
63184c517ca1b876d99b7e0e
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Xiaodong Cun
user
vinthony
65a8938c2984fa7203d6cf96
Menghan Xia
admin_assigned
2024-01-18T10:10:29.457Z
63401c89f81b9d101361f712
https://cdn-avatars.huggingface.co/v1/production/uploads/1665146415483-63401c89f81b9d101361f712.png
Richard
user
menghanxia
65a8938c2984fa7203d6cf97
Xintao Wang
claimed_verified
2024-01-18T08:19:38.538Z
60e272ca6c78a8c122b12127
https://cdn-avatars.huggingface.co/v1/production/uploads/60e272ca6c78a8c122b12127/xldEGBzGrU-bX6IwAw0Ie.jpeg
Xintao Wang
user
Xintao
65a8938c2984fa7203d6cf98
Chao Weng
65a8938c2984fa7203d6cf99
Ying Shan
admin_assigned
2024-01-18T10:11:09.585Z
63ca3ddc04c979828310bfcb
/avatars/615e0d8622950b4408b40d550f02a894.svg
Ying Shan
user
yshan2u
VideoCrafter2: Overcoming Data Limitations for High-Quality Video
  Diffusion Models
Text-to-video generation aims to produce a video based on a given prompt.
Recently, several commercial video models have been able to generate plausible
videos with minimal noise, excellent details, and high aesthetic scores.
However, these models rely on large-scale, well-filtered, high-quality videos
that are not accessible to the community. Many existing research works, which
train models using the low-quality WebVid-10M dataset, struggle to generate
high-quality videos because the models are optimized to fit WebVid-10M. In this
work, we explore the training scheme of video models extended from Stable
Diffusion and investigate the feasibility of leveraging low-quality videos and
synthesized high-quality images to obtain a high-quality video model. We first
analyze the connection between the spatial and temporal modules of video models
and the distribution shift to low-quality videos. We observe that full training
of all modules results in a stronger coupling between spatial and temporal
modules than only training temporal modules. Based on this stronger coupling,
we shift the distribution to higher quality without motion degradation by
finetuning spatial modules with high-quality images, resulting in a generic
high-quality video model. Evaluations are conducted to demonstrate the
superiority of the proposed method, particularly in picture quality, motion,
and concept composition.
A method is proposed to generate high-quality videos using low-quality videos and synthesized images by fine-tuning spatial modules of video models extended from Stable Diffusion.
text-to-video generation Stable Diffusion WebVid-10M video models spatial modules temporal modules distribution shift fine-tuning high-quality video model picture quality motion concept composition