Watch to Edit: Video Retargeting using Gaze

TL;DR

This paper introduces a novel gaze-based video retargeting method that optimizes cropping, panning, and zooming to preserve salient content across different display aspect ratios, improving viewer experience.

Contribution

The authors propose a content-agnostic, gaze-driven video editing algorithm that re-edits entire movies with optimized cut, pan, and zoom operations using convex optimization techniques.

Findings

Significant improvement over state-of-the-art in computational efficiency

Enhanced qualitative viewing experience confirmed by user study

Effective re-editing of wide-angle static camera videos

Abstract

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.