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

M3L: Language-based Video Editing via Multi-Modal Multi-Level Transformers