Paper Info
Title
Watch to Edit: Video Retargeting using Gaze.
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 [JSSH15] and letterboxing methods, especially for wide-angle static camera recordings.
Year
DOI
Venue
2018
10.1111/cgf.13354
COMPUTER GRAPHICS FORUM
DocType
Volume
Issue
Journal
37.0
2.0
ISSN
Citations 
PageRank 
0167-7055
3
0.46
References 
Authors
5
4
Name
Order
Citations
PageRank
Kranthi Kumar Rachavarapu131.14
Moneish Kumar230.46
Vineet Gandhi3279.21
Ramanathan Subramanian446122.16