filename     : Lan19a.pdf
entry        : article
conference   : Eurographics 2019
pages        : 393-403
year         : 2019
month        : 
title        : Controlling Motion Blur in Synthetic Long Time Exposures
subtitle     :
author       : Marcel Lancelle and Pelin Dogan and Markus Gross
booktitle    : Computer Graphics Forum (Proceedings of Eurographics 2019)
ISSN/ISBN    :
editor       : 
publisher    : The Eurographics Association and John Wiley & Sons Ltd.
publ.place   :
volume       : 38
issue        : 2
language     : English
keywords     : computational photography, image processing
abstract     : In a photo, motion blur can be used as an artistic style to convey motion and to direct attention. In panning or tracking shots, a moving object of interest is followed by the camera during a relatively long exposure. The goal is to get a blurred background while keeping the object sharp. Unfortunately, it can be difficult to impossible to precisely follow the object. Often, many attempts or specialized physical setups are needed. This paper presents a novel approach to create such images. For capturing, the user is only required to take a casually recorded hand-held video that roughly follows the object. %with a sharp moving object in front of a motion blurred background. This style can be used both to convey motion and to emphasize the moving object. Our algorithm then produces a single image which simulates a stabilized long time exposure. This is achieved by first warping all frames such that the object of interest is aligned to a reference frame. Then, optical flow based frame interpolation is used to reduce ghosting artifacts from temporal undersampling. Finally, the frames are averaged to create the result. As our method avoids segmentation and requires little to no user interaction, even challenging sequences can be processed successfully. In addition, artistic control is available in a number of ways. The effect can also be applied to create videos with an exaggerated motion blur. Results are compared with previous methods and ground truth simulations. The effectiveness of our method is demonstrated by applying it to hundreds of datasets. The most interesting results are shown in the paper and in the supplemental material.