Automatic Non-Linear Video Editing Transfer
This work addresses the challenge of automating video editing for creators, though it appears incremental as it builds on existing computer vision techniques.
The paper tackles the problem of automatically transferring editing styles from professionally edited videos to raw footage, achieving effective results as demonstrated through evaluation on 3872 video shots and positive feedback from survey participants.
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.