# Videogenic: Identifying Highlight Moments in Videos with Professional Photographs as a Prior

arXiv: https://arxiv.org/abs/2211.12493 · HTML: https://mlsanity.com/paper/2211.12493/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 42 references that are in the map.
- First posted on arXiv 22 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [Less is More: Learning Highlight Detection from Video Duration](https://mlsanity.com/paper/1903.00859/) (2019)
- [Video2GIF: Automatic Generation of Animated GIFs from Video](https://mlsanity.com/paper/1605.04850/) (2016)
- [To Click or Not To Click: Automatic Selection of Beautiful Thumbnails from Videos](https://mlsanity.com/paper/1609.01388/) (2016)
- [Learning Personal Style from Few Examples](https://mlsanity.com/paper/2105.14457/) (2021)

## Built on by (most cited first, 1 of 1)

- [When and How to Cut Classical Concerts? A Multimodal Automated Video Editing Approach](https://mlsanity.com/paper/2510.05661/) (2025)

## Abstract (arXiv)

This paper investigates the challenge of extracting highlight moments from videos. To perform this task, we need to understand what constitutes a highlight for arbitrary video domains 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 technique capable of creating domain-specific highlight videos for a diverse range of domains. In a human evaluation study (N=50), we show that a high-quality photograph collection combined with CLIP-based retrieval (which uses 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.