StyleMaster: Stylize Your Video with Artistic Generation and Translation
Style control has been popular in video generation models. Existing methods
often generate videos far from the given style, cause content leakage, and
struggle to transfer one video to the desired style. Our first observation is
that the style extraction stage matters, whereas existing methods emphasize
global style but ignore local textures. In order to bring texture features
while preventing content leakage, we filter content-related patches while
retaining style ones based on prompt-patch similarity; for global style
extraction, we generate a paired style dataset through model illusion to
facilitate contrastive learning, which greatly enhances the absolute style
consistency. Moreover, to fill in the image-to-video gap, we train a
lightweight motion adapter on still videos, which implicitly enhances
stylization extent, and enables our image-trained model to be seamlessly
applied to videos. Benefited from these efforts, our approach, StyleMaster, not
only achieves significant improvement in both style resemblance and temporal
coherence, but also can easily generalize to video style transfer with a gray
tile ControlNet. Extensive experiments and visualizations demonstrate that
StyleMaster significantly outperforms competitors, effectively generating
high-quality stylized videos that align with textual content and closely
resemble the style of reference images. Our project page is at
https://zixuan-ye.github.io/stylemaster
67597f9444ec15bfe4c6be7d
Zixuan Ye
claimed_verified
2024-12-11T13:39:09.531Z
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Zixuan Ye
user
zixuan-ye
67597f9444ec15bfe4c6be7e
Huijuan Huang
67597f9444ec15bfe4c6be7f
Xintao Wang
admin_assigned
2024-12-12T09:00:35.189Z
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Xintao Wang
user
Xintao
67597f9444ec15bfe4c6be80
Pengfei Wan
claimed_verified
2025-03-17T14:43:04.996Z
662f93942510ef5735d7ad00
https://cdn-avatars.huggingface.co/v1/production/uploads/662f93942510ef5735d7ad00/ZIDIPm63sncIHFTT5b0uR.png
magicwpf
user
magicwpf
67597f9444ec15bfe4c6be81
Di Zhang
admin_assigned
2024-12-12T09:00:50.168Z
64bce15bafd1e46c5504ad38
https://cdn-avatars.huggingface.co/v1/production/uploads/64bce15bafd1e46c5504ad38/vkEjiu-mIagKlrXzDH75o.png
Di Zhang
user
di-zhang-fdu
67597f9444ec15bfe4c6be82
Wenhan Luo
admin_assigned
2024-12-12T09:00:55.592Z
65f865cf806814aec87d45ce
/avatars/6b931c1efe20422a5985fce6a9e36aed.svg
Wenhan Luo
user
whluo
StyleMaster: Stylize Your Video with Artistic Generation and Translation
Style control has been popular in video generation models. Existing methods
often generate videos far from the given style, cause content leakage, and
struggle to transfer one video to the desired style. Our first observation is
that the style extraction stage matters, whereas existing methods emphasize
global style but ignore local textures. In order to bring texture features
while preventing content leakage, we filter content-related patches while
retaining style ones based on prompt-patch similarity; for global style
extraction, we generate a paired style dataset through model illusion to
facilitate contrastive learning, which greatly enhances the absolute style
consistency. Moreover, to fill in the image-to-video gap, we train a
lightweight motion adapter on still videos, which implicitly enhances
stylization extent, and enables our image-trained model to be seamlessly
applied to videos. Benefited from these efforts, our approach, StyleMaster, not
only achieves significant improvement in both style resemblance and temporal
coherence, but also can easily generalize to video style transfer with a gray
tile ControlNet. Extensive experiments and visualizations demonstrate that
StyleMaster significantly outperforms competitors, effectively generating
high-quality stylized videos that align with textual content and closely
resemble the style of reference images. Our project page is at
https://zixuan-ye.github.io/stylemaster
StyleMaster generates high-quality, style-consistent videos by filtering local textures, leveraging contrastive learning for global style, and training a lightweight motion adapter on still images.
style extraction content leakage texture features prompt-patch similarity paired style dataset model illusion contrastive learning style consistency image-to-video gap motion adapter style resemblance temporal coherence video style transfer gray tile ControlNet