# GAZED- Gaze-guided Cinematic Editing of Wide-Angle Monocular Video Recordings

arXiv: https://arxiv.org/abs/2010.11886 · HTML: https://mlsanity.com/paper/2010.11886/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 11 of 11)

- [Dynamic Storyboard Generation in an Engine-based Virtual Environment for Video Production](https://mlsanity.com/paper/2301.12688/) (2023)
- [TinyHD: Efficient Video Saliency Prediction with Heterogeneous Decoders using Hierarchical Maps Distillation](https://mlsanity.com/paper/2301.04619/) (2023)
- [CinePreGen: Camera Controllable Video Previsualization via Engine-powered Diffusion](https://mlsanity.com/paper/2408.17424/) (2024)
- [Reframe Anything: LLM Agent for Open World Video Reframing](https://mlsanity.com/paper/2403.06070/) (2024)
- [Minimalistic Video Saliency Prediction via Efficient Decoder & Spatio Temporal Action Cues](https://mlsanity.com/paper/2502.00397/) (2025)
- [Real Time GAZED: Online Shot Selection and Editing of Virtual Cameras from Wide-Angle Monocular Video Recordings](https://mlsanity.com/paper/2311.15581/) (2023)
- [SST-EM: Advanced Metrics for Evaluating Semantic, Spatial and Temporal Aspects in Video Editing](https://mlsanity.com/paper/2501.07554/) (2025)
- [EditIQ: Automated Cinematic Editing of Static Wide-Angle Videos via Dialogue Interpretation and Saliency Cues](https://mlsanity.com/paper/2502.02172/) (2025)
- [ComVi: Context-Aware Optimized Comment Display in Video Playback](https://mlsanity.com/paper/2603.26173/) (2026)
- [ViASNet: A Video Ad Saliency Network for Predicting Dynamic Saliency and Viewer Engagement](https://mlsanity.com/paper/2605.29302/) (2026)
- [Autoregressive Modeling of Film with Applications in Video Montage](https://mlsanity.com/paper/2607.14645/) (2026)

## Abstract (arXiv)

We present GAZED- eye GAZe-guided EDiting for videos captured by a solitary, static, wide-angle and high-resolution camera. Eye-gaze has been effectively employed in computational applications as a cue to capture interesting scene content; we employ gaze as a proxy to select shots for inclusion in the edited video. Given the original video, scene content and user eye-gaze tracks are combined to generate an edited video comprising cinematically valid actor shots and shot transitions to generate an aesthetic and vivid representation of the original narrative. We model cinematic video editing as an energy minimization problem over shot selection, whose constraints capture cinematographic editing conventions. Gazed scene locations primarily determine the shots constituting the edited video. Effectiveness of GAZED against multiple competing methods is demonstrated via a psychophysical study involving 12 users and twelve performance videos.