31–40 of 51 posts
I wonder when will we get this quality http://pages.cs.wisc.edu/~fliu/project/3dstab.htm
They should apply this during movie fighting scenes. Then we might actually see the fight instead of the blur caused by "exciting cameras".
Seems to work well- I just tried it on a tiger cub vid, which I've had for years and was taken with an el-cheapo digital camera: Shaky version: http://youtu.be/g5P9WwHdOSI Stabilised version http://youtu.be/wXfbRUk_1Bg There is some motion blur (around 24 seconds, when the momma tiger lies down) which is a bit puzzling once the image has been stabilised. But hey, it's better than it was before.
It also looks like the stabilized video is magnified a little bit. Regardless, these tools are really neat and they are still in their infancy. It's good to see that GooTube still believes in user-generated content.
I wonder when will we get this quality http://pages.cs.wisc.edu/~fliu/project/3dstab.htm
Shows how far we are in this whole cloud era.
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Probably a staged setup, inducing a regular shaking pattern, but I think generating rolling shutter algorithmicly would be a stretch for them, so I call physical but maybe voluntary shake.
Or maybe it's due to the 'rolling' shutter causing shear stretching as mentioned in the article
Earlier quoted context omitted.
This is amazing. Wonder if there is a commercial implementation yet?
These guys do some pretty amazing stuff, video stabilization is one of the applications: http://www.2d3.com/
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.