title: INVE: Interactive Neural Video Editing
summary: We present Interactive Neural Video Editing (INVE), a real-time video editing
  solution, which can assist the video editing process by consistently
  propagating sparse frame edits to the entire video clip. Our method is inspired
  by the recent work on Layered Neural Atlas (LNA). LNA, however, suffers from
  two major drawbacks: (1) the method is too slow for interactive editing, and
  (2) it offers insufficient support for some editing use cases, including direct
  frame editing and rigid texture tracking. To address these challenges we
  leverage and adopt highly efficient network architectures, powered by
  hash-grids encoding, to substantially improve processing speed. In addition, we
  learn bi-directional functions between image-atlas and introduce vectorized
  editing, which collectively enables a much greater variety of edits in both the
  atlas and the frames directly. Compared to LNA, our INVE reduces the learning
  and inference time by a factor of 5, and supports various video editing
  operations that LNA cannot. We showcase the superiority of INVE over LNA in
  interactive video editing through a comprehensive quantitative and qualitative
  analysis, highlighting its numerous advantages and improved performance. For
  video results, please see https://gabriel-huang.github.io/inve/
paper_authors._id: 64b6164921c601b486d3a674
paper_authors.name: Jiahui Huang
paper_authors.status: claimed_verified
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paper_authors.fullname: Gabriel Huang
paper_authors.type: user
paper_authors.user: GabrielH
paper_authors._id: 64b6164921c601b486d3a675
paper_authors.name: Leonid Sigal
paper_authors._id: 64b6164921c601b486d3a676
paper_authors.name: Kwang Moo Yi
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paper_authors.fullname: Kwang Moo Yi
paper_authors.type: user
paper_authors.user: kmyi
paper_authors._id: 64b6164921c601b486d3a677
paper_authors.name: Oliver Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-18T08:13:37.661Z
paper_authors._id: 62fa8bfdc95d426ff8f25fe9
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paper_authors.fullname: Oliver Wang
paper_authors.type: user
paper_authors.user: oliverwang
paper_authors._id: 64b6164921c601b486d3a678
paper_authors.name: Joon-Young Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-19T06:28:22.630Z
paper_authors._id: 64b76c7990b38df83381824b
paper_authors.avatarUrl: /avatars/0a5bec2ea480fb3f43c4b24d59c50e81.svg
paper_authors.fullname: Joon-Young Lee
paper_authors.type: user
paper_authors.user: joonyounglee
paper_title: INVE: Interactive Neural Video Editing
paper_summary: We present Interactive Neural Video Editing (INVE), a real-time video editing
  solution, which can assist the video editing process by consistently
  propagating sparse frame edits to the entire video clip. Our method is inspired
  by the recent work on Layered Neural Atlas (LNA). LNA, however, suffers from
  two major drawbacks: (1) the method is too slow for interactive editing, and
  (2) it offers insufficient support for some editing use cases, including direct
  frame editing and rigid texture tracking. To address these challenges we
  leverage and adopt highly efficient network architectures, powered by
  hash-grids encoding, to substantially improve processing speed. In addition, we
  learn bi-directional functions between image-atlas and introduce vectorized
  editing, which collectively enables a much greater variety of edits in both the
  atlas and the frames directly. Compared to LNA, our INVE reduces the learning
  and inference time by a factor of 5, and supports various video editing
  operations that LNA cannot. We showcase the superiority of INVE over LNA in
  interactive video editing through a comprehensive quantitative and qualitative
  analysis, highlighting its numerous advantages and improved performance. For
  video results, please see https://gabriel-huang.github.io/inve/
paper_ai_keywords: Interactive Neural Video Editing (INVE) Layered Neural Atlas (LNA) hash-grids encoding bi-directional functions vectorized editing interactive editing continuous LNA quantitative analysis qualitative analysis