Motion denoising with application to time-lapse photography
Author(s)Rubinstein, Michael; Liu, Ce; Sand, Peter; Durand, Fredo; Freeman, William T.
MetadataShow full item record
Motions can occur over both short and long time scales. We introduce motion denoising, which treats short-term changes as noise, long-term changes as signal, and re-renders a video to reveal the underlying long-term events. We demonstrate motion denoising for time-lapse videos. One of the characteristics of traditional time-lapse imagery is stylized jerkiness, where short-term changes in the scene appear as small and annoying jitters in the video, often obfuscating the underlying temporal events of interest. We apply motion denoising for resynthesizing time-lapse videos showing the long-term evolution of a scene with jerky short-term changes removed. We show that existing filtering approaches are often incapable of achieving this task, and present a novel computational approach to denoise motion without explicit motion analysis. We demonstrate promising experimental results on a set of challenging time-lapse sequences.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Institute of Electrical and Electronics Engineers (IEEE)
Rubinstein, Michael, Ce Liu, Peter Sand, Fredo Durand, and William T. Freeman. “Motion Denoising with Application to Time-Lapse Photography.” CVPR 2011 (n.d.).
Author's final manuscript