Automatic Non-Linear Video Editing Transfer

TL;DR

This paper introduces an automatic method for transferring editing styles from professionally edited videos to raw footage using computer vision techniques, considering multiple visual and temporal features.

Contribution

It presents a novel approach that automatically extracts and applies complex editing styles to raw videos, enhancing video creation efficiency.

Findings

Successfully transferred styles across diverse video types

Evaluated with 3872 video shots from real-world footage

Received positive feedback from user surveys

Abstract

We propose an automatic approach that extracts editing styles in a source video and applies the edits to matched footage for video creation. Our Computer Vision based techniques considers framing, content type, playback speed, and lighting of each input video segment. By applying a combination of these features, we demonstrate an effective method that automatically transfers the visual and temporal styles from professionally edited videos to unseen raw footage. We evaluated our approach with real-world videos that contained a total of 3872 video shots of a variety of editing styles, including different subjects, camera motions, and lighting. We reported feedback from survey participants who reviewed a set of our results.