Generating Highlight Videos of a User-Specified Length using Most Replayed Data

要旨

A highlight is a short edit of the original video that includes the most engaging moments. Given the rigid timing of TV commercial slots and length limits of social media uploads, generating highlights of specific lengths is crucial. Previous research on automatic highlight generation often overlooked the control over the duration of the final video, producing highlights of arbitrary lengths. We propose a novel system that automatically generates highlights of any user-specified length. Our system leverages Most Replayed Data (MRD), which identifies how frequently a video has been watched over time, to gauge the most engaging parts. It then optimizes the final editing path by adjusting internal segment durations. We evaluated the quality of our system's outputs through two user studies, including a comparison with highlights created by human editors. Results show that our system can automatically produce highlights that are indistinguishable from those created by humans in viewing experience.

著者
Minsun Kim
KAIST, Daejeon, Korea, Republic of
Dawon Lee
KAIST, Daejeon, Korea, Republic of
Junyong Noh
KAIST, Daejeon, Korea, Republic of
DOI

10.1145/3706598.3713880

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713880

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Video Making

G303
7 件の発表
2025-04-29 23:10:00
2025-04-30 00:40:00
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