title: AnyV2V: A Plug-and-Play Framework For Any Video-to-Video Editing Tasks
summary: Video-to-video editing involves editing a source video along with additional
  control (such as text prompts, subjects, or styles) to generate a new video
  that aligns with the source video and the provided control. Traditional methods
  have been constrained to certain editing types, limiting their ability to meet
  the wide range of user demands. In this paper, we introduce AnyV2V, a novel
  training-free framework designed to simplify video editing into two primary
  steps: (1) employing an off-the-shelf image editing model (e.g.
  InstructPix2Pix, InstantID, etc) to modify the first frame, (2) utilizing an
  existing image-to-video generation model (e.g. I2VGen-XL) for DDIM inversion
  and feature injection. In the first stage, AnyV2V can plug in any existing
  image editing tools to support an extensive array of video editing tasks.
  Beyond the traditional prompt-based editing methods, AnyV2V also can support
  novel video editing tasks, including reference-based style transfer,
  subject-driven editing, and identity manipulation, which were unattainable by
  previous methods. In the second stage, AnyV2V can plug in any existing
  image-to-video models to perform DDIM inversion and intermediate feature
  injection to maintain the appearance and motion consistency with the source
  video. On the prompt-based editing, we show that AnyV2V can outperform the
  previous best approach by 35\% on prompt alignment, and 25\% on human
  preference. On the three novel tasks, we show that AnyV2V also achieves a high
  success rate. We believe AnyV2V will continue to thrive due to its ability to
  seamlessly integrate the fast-evolving image editing methods. Such
  compatibility can help AnyV2V to increase its versatility to cater to diverse
  user demands.
paper_authors._id: 65fcf07da2d58888315a2e97
paper_authors.name: Max Ku
paper_authors.status: claimed_verified
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paper_authors.fullname: Max Ku
paper_authors.type: user
paper_authors.user: vinesmsuic
paper_authors._id: 65fcf07da2d58888315a2e98
paper_authors.name: Cong Wei
paper_authors.status: admin_assigned
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paper_authors.fullname: Cong Wei
paper_authors.type: user
paper_authors.user: lim142857
paper_authors._id: 65fcf07da2d58888315a2e99
paper_authors.name: Weiming Ren
paper_authors.status: claimed_verified
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paper_authors.fullname: Weiming Ren
paper_authors.type: user
paper_authors.user: wren93
paper_authors._id: 65fcf07da2d58888315a2e9a
paper_authors.name: Huan Yang
paper_authors._id: 65fcf07da2d58888315a2e9b
paper_authors.name: Wenhu Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-22T09:42:05.786Z
paper_authors._id: 6313a86154e6e5d9f0f94e04
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paper_authors.fullname: Wenhu Chen
paper_authors.type: user
paper_authors.user: wenhu
paper_title: AnyV2V: A Plug-and-Play Framework For Any Video-to-Video Editing Tasks
paper_summary: Video-to-video editing involves editing a source video along with additional
  control (such as text prompts, subjects, or styles) to generate a new video
  that aligns with the source video and the provided control. Traditional methods
  have been constrained to certain editing types, limiting their ability to meet
  the wide range of user demands. In this paper, we introduce AnyV2V, a novel
  training-free framework designed to simplify video editing into two primary
  steps: (1) employing an off-the-shelf image editing model (e.g.
  InstructPix2Pix, InstantID, etc) to modify the first frame, (2) utilizing an
  existing image-to-video generation model (e.g. I2VGen-XL) for DDIM inversion
  and feature injection. In the first stage, AnyV2V can plug in any existing
  image editing tools to support an extensive array of video editing tasks.
  Beyond the traditional prompt-based editing methods, AnyV2V also can support
  novel video editing tasks, including reference-based style transfer,
  subject-driven editing, and identity manipulation, which were unattainable by
  previous methods. In the second stage, AnyV2V can plug in any existing
  image-to-video models to perform DDIM inversion and intermediate feature
  injection to maintain the appearance and motion consistency with the source
  video. On the prompt-based editing, we show that AnyV2V can outperform the
  previous best approach by 35\% on prompt alignment, and 25\% on human
  preference. On the three novel tasks, we show that AnyV2V also achieves a high
  success rate. We believe AnyV2V will continue to thrive due to its ability to
  seamlessly integrate the fast-evolving image editing methods. Such
  compatibility can help AnyV2V to increase its versatility to cater to diverse
  user demands.
paper_ai_keywords: image editing model InstructPix2Pix InstantID image-to-video generation model I2VGen-XL DDIM inversion feature injection prompt-based editing reference-based style transfer identity manipulation human preference appearance and motion consistency