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VideoDiff: Human-AI Video Co-Creation with Alternatives

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arxiv 2502.10190 v1 pith:TVUBWDL4 submitted 2025-02-14 cs.HC

VideoDiff: Human-AI Video Co-Creation with Alternatives

classification cs.HC
keywords alternativeseditingvideovideodiffcomparecreatorsb-rollscreating
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. AnimationDiff: A Visual Comparison Tool for Generated 3D Character Animations

    cs.HC 2026-05 unverdicted novelty 5.0

    AnimationDiff is a visual comparison tool that combines contextual scene viewing, overlay/side-by-side modes, filtering, and temporal lenses to help users select among generated 3D character animations.

  2. GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Design

    cs.HC 2025-08 conditional novelty 5.0

    GenTune improves AI image refinement by tracing image regions back to prompt labels and allowing element-level, semantic-guided edits.