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

Autor:	Lin, David Chuan-En, Heilbron, Fabian Caba, Lee, Joon-Young, Wang, Oliver, Martelaro, Nikolas
Rok vydání:	2022
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Human-Computer Interaction Computer Science - Multimedia
Druh dokumentu:	Working Paper
DOI:	10.1145/3635636.3656186
Popis:	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. Comment: https://humanvideointeraction.github.io/videogenic
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.12493 Zobrazit plný text záznamu View this record from Arxiv