As mentioned in the blogpost, the rolling-shutter version of this won the best paper prize at the International Conference on Computation Photography (ICCP), wh

As mentioned in the blogpost, the rolling-shutter version of this won the best paper prize at the International Conference on Computation Photography (ICCP), which was held last weekend in Seattle. This is a fairly new but very high quality conference. In many respects, I prefer it to the standard-bearing vision conferences like CVPR, ICCV, or ECCV -- although of course, ICCP is more narrowly focused on computational imaging and photography applications. In their talk, the authors of this work showed many more video results and they were all quite impressive. In fact, they were good enough to fall into an "uncanny valley of motion", similar to the "uncanny valley" of faces or humans [1] that most people are familiar with. I.e., the motion correction was almost perfect, but just enough off that something felt vaguely surreal about the results. Nevertheless, it's a nice step forward. Also, as others have pointed out, this is a fully uncalibrated method -- requiring no knowledge of how the video was captured. If you do have some knowledge, then you can often exploit it to do better. But the authors mentioned that most videos uploaded to youtube have either no calibration information, or if present, it's often incorrect. As such, it made sense for them to focus on the uncalibrated case. Finally, I should point out that rolling shutter, standard on most mobile cameras, is causing all sorts of problems for traditional image and video analysis algorithms, which often make the assumption, sometimes implicitly, that the entire frame was captured at a single instance in time. This is not true anymore, and can lead to gross errors in many methods. Hence the many recent papers on correcting for, and in some cases exploiting, rolling shutter effects. A recent interesting work along these lines from my former lab at Columbia University is "coded rolling shutter photography: flexible space-time photography" [2]. This paper takes advantage of the fact that different rows in an image are seeing the world at slightly different instances in time to do things like high-speed photography, HDR imaging, etc. [1] http://en.wikipedia.org/wiki/Uncanny_valley http://en.wikipedia.org/wiki/Uncanny_valley [2] http://www.cs.columbia.edu/CAVE/projects/crsp/ http://www.cs.columbia.edu/CAVE/projects/crsp/