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3-Sweep: Extracting Editable Objects from a Single Photo [video]

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Re: 3-Sweep: Extracting Editable Objects from a Single Photo [video]

#41
post #21

Is there a reason many of these crazy image processing technologies never seem to have actual demos or releases? The only exception I can think of it the "smart erase" idea, which has been implemented in Photoshop as well as Gimp.

What other image manipulation software do you follow closely?

I don't follow any closely, I just remember seeing several tech demos similar to this.

Re: 3-Sweep: Extracting Editable Objects from a Single Photo [video]

#42
I'm not a HN etiquette stickler, and I'm not accusing anyone of any foul play, but the actual YouTube video was submitted 17 hours prior to this post: https://news.ycombinator.com/item?id=6358080

This is just in case you want to throw a few upvotes their way for being first. This also illustrates that late night (PDT/UTC -8) posts don't get a whole lot of votes and proper timing is crucial to getting lots of votes.

Re: 3-Sweep: Extracting Editable Objects from a Single Photo [video]

#43
post #39

Wow, super impressive. And meanwhile, Silicon Valley is working on the gazillionth social photo sharing app.

The other side of the argument is that social networks improve far more lives than academic research projects like this.

Re: 3-Sweep: Extracting Editable Objects from a Single Photo [video]

#45
post #30
post #28

This is awesome - but how do they reconstruct the backgrounds that the objects previously obscured? There must be more photos?

i thought about that too - i think the background is simply a mirror image of the foreground, and that the object 3d shape is symmetrical

apparently they are using some other algorithm to do this - even more impressive!

however, it seems strange in the first example how mountain ranges appear where none were before... how did the algos know to put it there?

Re: 3-Sweep: Extracting Editable Objects from a Single Photo [video]

#46

The key here is really complementary use of ‘what humans are good at’ and ‘what machines are good at’. In this case, it’s fair to say the machine, by analyzing pixels, can’t figure out perspective very well. The human can do that just fine, given an interface mechanism. The machine is good at detecting edges and seeing similarity between pixels. Given hints from the human that ‘this point is within an object’ and her…

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Re: 3-Sweep: Extracting Editable Objects from a Single Photo [video]

#47
post #42

I'm not a HN etiquette stickler, and I'm not accusing anyone of any foul play, but the actual YouTube video was submitted 17 hours prior to this post: https://news.ycombinator.com/item?id=6358080 This is just in case you want to throw a few upvotes their way for being first. This also illustrates that late night (PDT/UTC -8) posts don't get a whole lot of votes and proper timing is crucial to getting lots of votes.

It was also submitted here even earlier: https://news.ycombinator.com/item?id=6351712

Personally, I'm just glad to see this video finally getting traction. It really is such a cool demo. It even stands out in the field of consistently high-quality SIGGRAPH demos. Can't wait to read the paper!

Re: 3-Sweep: Extracting Editable Objects from a Single Photo [video]

#48
post #20

Earlier quoted context omitted.

If you're just removing part of the image after cutting around it with a tool like this, having the object interpreted as 3D isn't really going to be of any benefit. The impressive thing here, imho, is the seemingly effortless and seamless transition and replacement. The background is fixed and the surface texture is stretched in what seems like real time.

Yes... I know the 3D part is the more impressive part. But I was also impressed with its ability to back fill the background.

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Re: 3-Sweep: Extracting Editable Objects from a Single Photo [video]

#49
post #45
post #30

Earlier quoted context omitted.

i thought about that too - i think the background is simply a mirror image of the foreground, and that the object 3d shape is symmetrical

apparently they are using some other algorithm to do this - even more impressive! however, it seems strange in the first example how mountain ranges appear where none were before... how did the algos know to put it there?

This is the PatchMatch algorithm:

http://gfx.cs.princeton.edu/pubs/Barnes_2009_PAR/

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