# Towards Data-Driven Automatic Video Editing

## TL;DR

This paper presents a data-driven approach to automatic video editing that leverages neural networks and imitation learning to select and cut footage based on learned cinematography rules, aiming to produce engaging visual stories.

## Contribution

It introduces a novel method combining visual feature extraction and imitation learning for automatic video editing, mimicking professional editing principles.

## Key findings

- Controller learns basic cinematography editing rules
- Produces coherent and visually appealing video edits
- Demonstrates effectiveness on a corpus of motion pictures

## Abstract

Automatic video editing involving at least the steps of selecting the most valuable footage from points of view of visual quality and the importance of action filmed; and cutting the footage into a brief and coherent visual story that would be interesting to watch is implemented in a purely data-driven manner. Visual semantic and aesthetic features are extracted by the ImageNet-trained convolutional neural network, and the editing controller is trained by an imitation learning algorithm. As a result, at test time the controller shows the signs of observing basic cinematography editing rules learned from the corpus of motion pictures masterpieces.

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Source: https://tomesphere.com/paper/1907.07345