# Watch to Edit: Video Retargeting using Gaze

arXiv: https://arxiv.org/abs/1807.03125 · HTML: https://mlsanity.com/paper/1807.03125/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2018.

## Built on by (most cited first, 4 of 4)

- [Reviewing Intelligent Cinematography: AI research for camera-based video production](https://mlsanity.com/paper/2405.05039/) (2024)
- [Real Time GAZED: Online Shot Selection and Editing of Virtual Cameras from Wide-Angle Monocular Video Recordings](https://mlsanity.com/paper/2311.15581/) (2023)
- [MemX: An Attention-Aware Smart Eyewear System for Personalized Moment Auto-capture](https://mlsanity.com/paper/2105.00916/) (2021)
- [EditIQ: Automated Cinematic Editing of Static Wide-Angle Videos via Dialogue Interpretation and Saliency Cues](https://mlsanity.com/paper/2502.02172/) (2025)

## Abstract (arXiv)

We present a novel approach to optimally retarget videos for varied displays with differing aspect ratios by preserving salient scene content discovered via eye tracking. Our algorithm performs editing with cut, pan and zoom operations by optimizing the path of a cropping window within the original video while seeking to (i) preserve salient regions, and (ii) adhere to the principles of cinematography. Our approach is (a) content agnostic as the same methodology is employed to re-edit a wide-angle video recording or a close-up movie sequence captured with a static or moving camera, and (b) independent of video length and can in principle re-edit an entire movie in one shot. Our algorithm consists of two steps. The first step employs gaze transition cues to detect time stamps where new cuts are to be introduced in the original video via dynamic programming. A subsequent step optimizes the cropping window path (to create pan and zoom effects), while accounting for the original and new cuts. The cropping window path is designed to include maximum gaze information, and is composed of piecewise constant, linear and parabolic segments. It is obtained via L(1) regularized convex optimization which ensures a smooth viewing experience. We test our approach on a wide variety of videos and demonstrate significant improvement over the state-of-the-art, both in terms of computational complexity and qualitative aspects. A study performed with 16 users confirms that our approach results in a superior viewing experience as compared to gaze driven re-editing and letterboxing methods, especially for wide-angle static camera recordings.