[Paper Review] AI video editing tools. What editors want and how far is AI from delivering?

Than Htut Soe|arXiv (Cornell University)|Sep 16, 2021

Generative Adversarial Networks and Image Synthesis4 citations

TL;DR

This paper investigates the gap between professional video editors' automation needs and the current capabilities of AI video editing tools. By surveying 13 editors and reviewing AI literature, it identifies unmet needs in video logging, file organization, aesthetic enhancement, and content suggestion—highlighting voice interaction and personalization as key future directions, while noting that most existing tools are narrow in scope and rely on heuristics rather than machine learning.

ABSTRACT

Video editing can be a very tedious task, so unsurprisingly Artificial Intelligence has been increasingly used to streamline the workflow or automate away tedious tasks. However, it is very difficult to get an overview of what intelligent video editing tools are in the research literature and needs for automation from the video editors. So, we identified the field of intelligent video editing tools in research, and we survey the opinions of professional video editors. We have also summarized current state of the art in artificial intelligence research with the intention of identifying what are the possibilities and current technical limits towards truly intelligent video editing tools. The findings contribute towards understanding of the field of intelligent video editing tools, highlights unaddressed automation needs by the survey and provides general suggestions for further research in intelligent video editing tools.

Motivation & Objective

• To identify and analyze the automation needs of professional video editors in real-world workflows.
• To evaluate the current state of the art in AI-driven video editing tools against these professional expectations.
• To highlight underexplored areas in intelligent video editing, such as video logging, file organization, and aesthetic quality adjustments.
• To propose future research directions, particularly in voice-based interaction and personalization, based on editor feedback and AI capabilities.
• To bridge the interdisciplinary gap between video editing, human-computer interaction, and AI/machine learning research.

Proposed method

• Conducted a survey of 13 professional video editors with experience ranging from 1 to 22 years to assess their automation needs and expectations.
• Performed thematic analysis on survey responses to categorize desired automation tasks and interaction preferences.
• Reviewed existing literature on intelligent video editing tools and AI techniques in video processing, focusing on video editing-specific applications.
• Mapped current AI techniques—such as speech recognition, action detection, and video reasoning—against the automation needs identified in the survey.
• Evaluated the feasibility of voice-based interactions and personalization in AI video editors, noting their absence in current tools.
• Contrasted heuristic-based systems in existing tools with the potential of machine learning and neural networks for future development.

Experimental results

Research questions

• RQ1 What specific video editing tasks do professional editors want automated, and how do these differ from current AI tool capabilities?
• RQ2 How do video editors perceive the role of AI in streamlining their workflows, particularly in terms of interaction modes?
• RQ3 What are the key technical and design limitations of current intelligent video editing tools in addressing real-world editing needs?
• RQ4 In what ways can AI techniques such as speech recognition, video reasoning, and personalization enhance video editing automation?
• RQ5 Why is the integration of machine learning in video editing tools still limited despite advances in AI?

Key findings

• Video editors frequently desire automation for non-core tasks such as file and media organization, aesthetic quality improvements, and content suggestion—areas largely unaddressed by current AI tools.
• Voice-based interaction is the most desired mode of interaction among editors, yet only one existing tool supports voice input, indicating a significant research gap.
• Most intelligent video editing tools are designed for a single video type (e.g., dialogue-based or demonstration videos), limiting their generalizability across diverse editing workflows.
• Current AI tools rely heavily on heuristic-based systems rather than machine learning, which restricts adaptability and personalization in editing processes.
• There is a strong disconnect between the capabilities of state-of-the-art AI in vision, NLP, and audio processing and their integration into practical, user-centric video editing tools.
• Personalization—learning from user editing patterns and video outputs—was a recurring need in the survey but is absent in existing literature and tools.