  Video editing tools are widely used nowadays for digital design. Although the
demand for these tools is high, the prior knowledge required makes it difficult
for novices to get started. Systems that could follow natural language
instructions to perform automatic editing would significantly improve
accessibility. This paper introduces the language-based video editing (LBVE)
task, which allows the model to edit, guided by text instruction, a source
video into a target video. LBVE contains two features: 1) the scenario of the
source video is preserved instead of generating a completely different video;
2) the semantic is presented differently in the target video, and all changes
are controlled by the given instruction. We propose a Multi-Modal Multi-Level
Transformer (M$^3$L) to carry out LBVE. M$^3$L dynamically learns the
correspondence between video perception and language semantic at different
levels, which benefits both the video understanding and video frame synthesis.
We build three new datasets for evaluation, including two diagnostic and one
from natural videos with human-labeled text. Extensive experimental results
show that M$^3$L is effective for video editing and that LBVE can lead to a new
field toward vision-and-language research.
