VideoDiff: Human-AI Video Co-Creation with Alternatives

要旨

To make an engaging video, people sequence interesting moments and add visuals such as B-rolls or text. While video editing requires time and effort, AI has recently shown strong potential to make editing easier through suggestions and automation. A key strength of generative models is their ability to quickly generate multiple variations, but when provided with many alternatives, creators struggle to compare them to find the best fit. We propose VideoDiff, an AI video editing tool designed for editing with alternatives. With VideoDiff, creators can generate and review multiple AI recommendations for each editing process: creating a rough cut, inserting B-rolls, and adding text effects. VideoDiff simplifies comparisons by aligning videos and highlighting differences through timelines, transcripts, and video previews. Creators have the flexibility to regenerate and refine AI suggestions as they compare alternatives. Our study participants (N=12) could easily compare and customize alternatives, creating more satisfying results.

著者
Mina Huh
University of Texas, Austin, Austin, Texas, United States
Ding Li
Adobe Research, Seattle, Washington, United States
Kim Pimmel
Adobe, Seattle, Washington, United States
Hijung Valentina Shin
Adobe Research, Cambridge, Massachusetts, United States
Amy Pavel
University of Texas, Austin, Austin, Texas, United States
Mira Dontcheva
Adobe Research, Seattle, Washington, United States
DOI

10.1145/3706598.3713417

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713417

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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