Video deblurring for hand-held cameras using patch-based synthesis
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Cliplets: juxtaposing still and dynamic imagery
Proceedings of the 25th annual ACM symposium on User interface software and technology
Weakly supervised learning of object segmentations from web-scale video
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Online real-time presentation of virtual experiences forexternal viewers
Proceedings of the 18th ACM symposium on Virtual reality software and technology
Bundled camera paths for video stabilization
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Hybrid robotic/virtual pan-tilt-zom cameras for autonomous event recording
Proceedings of the 21st ACM international conference on Multimedia
Computer Vision and Image Understanding
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We present a novel algorithm for automatically applying constrainable, L1-optimal camera paths to generate stabilized videos by removing undesired motions. Our goal is to compute camera paths that are composed of constant, linear and parabolic segments mimicking the camera motions employed by professional cinematographers. To this end, our algorithm is based on a linear programming framework to minimize the first, second, and third derivatives of the resulting camera path. Our method allows for video stabilization beyond the conventional filtering of camera paths that only suppresses high frequency jitter. We incorporate additional constraints on the path of the camera directly in our algorithm, allowing for stabilized and retargeted videos. Our approach accomplishes this without the need of user interaction or costly 3D reconstruction of the scene, and works as a post-process for videos from any camera or from an online source.