adobe logo cmu logo neurips 2022 logo

Videogenic: Video Highlights via Photogenic Moments

David Chuan-En Lin1, Fabian Caba Heilbron2, Joon-Young Lee2, Oliver Wang2, Nikolas Martelaro1

1Carnegie Mellon University, 2Adobe Research

📄 Paper📝 Citation (BibTeX)


This paper investigates the challenge of extracting highlight moments out of videos. To perform this task, a system needs to understand what constitutes a highlight for a video domain while at the same time being able to scale across different domains. Our key insight is that photographs taken by photographers tend to capture the most remarkable or photogenic moments of an activity. Drawing on this insight, we present Videogenic, a system capable of creating domain-specific highlight videos for a wide range of domains. In a human evaluation study (N=50), we show that a high-quality photograph collection combined with encodings of CLIP, a neural network with semantic knowledge of images, can serve as an excellent prior for finding video highlights. In a within-subjects expert study (N=12), we demonstrate the usefulness of Videogenic in helping video editors create highlight videos with lighter workload, shorter task completion time, and better usability.

Example Results

Original videos:

Example Highlight Template