title: StableV2V: Stablizing Shape Consistency in Video-to-Video Editing
summary: Recent advancements of generative AI have significantly promoted content
  creation and editing, where prevailing studies further extend this exciting
  progress to video editing. In doing so, these studies mainly transfer the
  inherent motion patterns from the source videos to the edited ones, where
  results with inferior consistency to user prompts are often observed, due to
  the lack of particular alignments between the delivered motions and edited
  contents. To address this limitation, we present a shape-consistent video
  editing method, namely StableV2V, in this paper. Our method decomposes the
  entire editing pipeline into several sequential procedures, where it edits the
  first video frame, then establishes an alignment between the delivered motions
  and user prompts, and eventually propagates the edited contents to all other
  frames based on such alignment. Furthermore, we curate a testing benchmark,
  namely DAVIS-Edit, for a comprehensive evaluation of video editing, considering
  various types of prompts and difficulties. Experimental results and analyses
  illustrate the outperforming performance, visual consistency, and inference
  efficiency of our method compared to existing state-of-the-art studies.
paper_authors._id: 673c4bb46864fdcc715c8fd1
paper_authors.name: Chang Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-19T13:09:10.693Z
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paper_authors.fullname: Chang Liu
paper_authors.type: user
paper_authors.user: AlonzoLeeeooo
paper_authors._id: 673c4bb46864fdcc715c8fd2
paper_authors.name: Rui Li
paper_authors._id: 673c4bb46864fdcc715c8fd3
paper_authors.name: Kaidong Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-19T16:34:40.509Z
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paper_authors.avatarUrl: /avatars/900209c7c747914baf89175bcdb9f7c3.svg
paper_authors.fullname: Kaidong Zhang
paper_authors.type: user
paper_authors.user: hitachinsk
paper_authors._id: 673c4bb46864fdcc715c8fd4
paper_authors.name: Yunwei Lan
paper_authors._id: 673c4bb46864fdcc715c8fd5
paper_authors.name: Dong Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-19T16:34:48.363Z
paper_authors._id: 66b5dce0602c26aa8838ad3b
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paper_authors.fullname: Dong Liu
paper_authors.type: user
paper_authors.user: DongLiu666
paper_title: StableV2V: Stablizing Shape Consistency in Video-to-Video Editing
paper_summary: Recent advancements of generative AI have significantly promoted content
  creation and editing, where prevailing studies further extend this exciting
  progress to video editing. In doing so, these studies mainly transfer the
  inherent motion patterns from the source videos to the edited ones, where
  results with inferior consistency to user prompts are often observed, due to
  the lack of particular alignments between the delivered motions and edited
  contents. To address this limitation, we present a shape-consistent video
  editing method, namely StableV2V, in this paper. Our method decomposes the
  entire editing pipeline into several sequential procedures, where it edits the
  first video frame, then establishes an alignment between the delivered motions
  and user prompts, and eventually propagates the edited contents to all other
  frames based on such alignment. Furthermore, we curate a testing benchmark,
  namely DAVIS-Edit, for a comprehensive evaluation of video editing, considering
  various types of prompts and difficulties. Experimental results and analyses
  illustrate the outperforming performance, visual consistency, and inference
  efficiency of our method compared to existing state-of-the-art studies.
paper_ai_summary: StableV2V is a shape-consistent video editing method that decomposes the editing process into sequential steps to align motions with user prompts, outperforming existing methods in visual consistency and efficiency.
paper_ai_keywords: video editing shape-consistency alignment visual consistency inference efficiency DAVIS-Edit