  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/
