Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

AI Video Editing: a Survey

Version 1 : Received: 30 December 2021 / Approved: 4 January 2022 / Online: 4 January 2022 (15:43:04 CET)
Version 2 : Received: 4 February 2022 / Approved: 4 February 2022 / Online: 4 February 2022 (13:40:05 CET)

How to cite: Zhang, X.; Li, Y.; Han, Y.; Wen, J. AI Video Editing: a Survey. Preprints 2022, 2022010016. Zhang, X.; Li, Y.; Han, Y.; Wen, J. AI Video Editing: a Survey. Preprints 2022, 2022010016.


Video editing is a high-required job, for it requires skilled artists or workers equipped with plentiful physical strength and multidisciplinary knowledge, such as cinematography, aesthetics. Thus gradually, more and more researches focus on proposing semi-automatical and even fully automatical solutions to reduce workloads. Since those conventional methods are usually designed to follow some simple guidelines, they lack flexibility and capability to learn complex ones. Fortunately, the advances of computer vision and machine learning make up the shortages of traditional approaches and make AI editing feasible. There is no survey to conclude those emerging researches yet. This paper summaries the development history of automatic video editing, and especially the applications of AI in partial and full workflows. We emphasizes video editing and discuss related works from multiple aspects: modality, type of input videos, methology, optimization, dataset, and evaluation metric. Besides, we also summarize the progresses in image editing domain, i.e., style transferring, retargeting, and colorization, and seek for the possibility to transfer those techniques to video domain. Finally, we give a brief conclusion about this survey and explore some open problems.


AI; deep learning; video editing; image editing


Engineering, Control and Systems Engineering

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