VideoMap: Supporting Video Editing Exploration, Brainstorming, and Prototyping in the Latent Space
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Abstract (arXiv)
Video editing is a creative and complex endeavor and we believe that there is potential for reimagining a new video editing interface to better support the creative and exploratory nature of video editing. We take inspiration from latent space exploration tools that help users find patterns and connections within complex datasets. We present VideoMap, a proof-of-concept video editing interface that operates on video frames projected onto a latent space. We support intuitive navigation through map-inspired navigational elements and facilitate transitioning between different latent spaces through swappable lenses. We built three VideoMap components to support editors in three common video tasks. In a user study with both professionals and non-professionals, editors found that VideoMap helps reduce grunt work, offers a user-friendly experience, provides an inspirational way of editing, and effectively supports the exploratory nature of video editing. We further demonstrate the versatility of VideoMap by implementing three extended applications. For interactive examples, we invite you to visit our project page: https://humanvideointeraction.github.io/videomap.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 14 of its 77 references that are in the map. First posted on arXiv 22 Nov 2022.
Builds on
14 of 77 references.
References · 14
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
- 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
- 2019Text-based Editing of Talking-head Videocited by 103
- 2016Embedding Projector: Interactive Visualization and Interpretation of Embeddingscited by 44
- 2017Dynamic Word Embeddings for Evolving Semantic Discoverycited by 38
- 2019Embedding Comparator: Visualizing Differences in Global Structure and Local Neighborhoods via Small Multiplescited by 38
- 2018EmbeddingVis: A Visual Analytics Approach to Comparative Network Embedding Inspectioncited by 18
- 2019B-Script: Transcript-based B-roll Video Editing with Recommendationscited by 16
- 2022Emblaze: Illuminating Machine Learning Representations through Interactive Comparison of Embedding Spacescited by 16
- 2022Match Cutting: Finding Cuts with Smooth Visual Transitionscited by 12
- 2021Soundify: Matching Sound Effects to Videocited by 6
Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 1.
4 papers.
- 2024ExpressEdit: Video Editing with Natural Language and Sketchingcited by 14
- 2023Next Steps for Human-Centered Generative AI: A Technical Perspectivecited by 13
- 2025VideoMix: Aggregating How-To Videos for Task-Oriented Learningcited by 3
- 2026Vidmento: Creating Video Stories Through Context-Aware Expansion With Generative Videocited by 2
- 2026Vidmento: Creating Video Stories Through Context-Aware Expansion With Generative Video29 Jan 2026
- 2025VideoMix: Aggregating How-To Videos for Task-Oriented Learning27 Mar 2025
- 2024ExpressEdit: Video Editing with Natural Language and Sketching26 Mar 2024
- 2023Next Steps for Human-Centered Generative AI: A Technical Perspective27 Jun 2023