title: StyleMaster: Stylize Your Video with Artistic Generation and Translation
summary: 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
paper_authors._id: 67597f9444ec15bfe4c6be7d
paper_authors.name: Zixuan Ye
paper_authors.status: claimed_verified
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paper_authors.fullname: Ye
paper_authors.type: user
paper_authors.user: zixuan-ye
paper_authors._id: 67597f9444ec15bfe4c6be7e
paper_authors.name: Huijuan Huang
paper_authors._id: 67597f9444ec15bfe4c6be7f
paper_authors.name: Xintao Wang
paper_authors.status: admin_assigned
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paper_authors.fullname: Xintao Wang
paper_authors.type: user
paper_authors.user: Xintao
paper_authors._id: 67597f9444ec15bfe4c6be80
paper_authors.name: Pengfei Wan
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paper_authors.fullname: magicwpf
paper_authors.type: user
paper_authors.user: magicwpf
paper_authors._id: 67597f9444ec15bfe4c6be81
paper_authors.name: Di Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-12T09:00:50.168Z
paper_authors._id: 64bce15bafd1e46c5504ad38
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paper_authors.fullname: Di Zhang
paper_authors.type: user
paper_authors.user: di-zhang-fdu
paper_authors._id: 67597f9444ec15bfe4c6be82
paper_authors.name: Wenhan Luo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-12T09:00:55.592Z
paper_authors._id: 65f865cf806814aec87d45ce
paper_authors.avatarUrl: /avatars/6b931c1efe20422a5985fce6a9e36aed.svg
paper_authors.fullname: Wenhan Luo
paper_authors.type: user
paper_authors.user: whluo
paper_title: StyleMaster: Stylize Your Video with Artistic Generation and Translation
paper_summary: 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
paper_ai_keywords: style control video generation models style extraction computationally expensive content leakage prompt-patch similarity paired style dataset contrastive learning global style extraction motion adapter image-to-video gap style resemblance temporal coherence video style transfer textual content high-quality stylized videos reference images