Tried to remove an elephant in an hard instance (what to do with the woman on top?), and a bicycle in an easy instance (lone bicycle), both found from Google Image Search .
Terrible results in both. It looks like it just works on their examples and doesn't do any better than object detection + paint a solid rectangle on others (arguably, it does worse than that).
unfortunately in my experience that's the case for most of opensource AI projects out there, while the showcase results are hand-picked or the algorithms was trained and tuned to solve that specific image.
I doubt this AI gets perfect performance even in the training set. Deep generative models are known to underfit more rather than overfit, i.e. they can't even do a good job of the full training set let alone the test set. The cherry-picked examples you see are just statistical outliers corresponding to VERY easy examples.
Statistically-random excellent performance in complex tasks is very unlikely. More likely is that examples are in-sample from a small training set or very similar. Big NNs can memorize anything.
They can memorize any supervised learning task, but so far, we haven't been able to see any deep generative model successfully memorize something more complex than MNIST.
damn this is embarrassing considering how much PR and marketing MIT puts out. That image is almost comical, it's as if somebody just got tired and holds a loose definition of "removing an object from a picture".
That's a super fair question. Depending on the image, Deep Angel can produce a quite plausible background but sometimes it just fills in the object with a "gray blob." The gray blob issue can arise from (1) the object is too big relative to the rest of the image. For example, consider an image in which 67% of it is made up by the object that you wish to remove. In this case, DeepFill doesn't have enough context to fill in the pixels in a plausible manner. (2) the object is on the side of the image. The further skewed the object is from the center, the less information DeepFill has to plausibly inpaint. (3) the training data is quite different from the test data.
The gray blob is a collapse of the pixels to the mean of the colors and textures around the removed portion of the photo.
The AI isn't yet perfect, and as you use the AI, you'll start to see which kind of photographs work really well and which do not.
Look at the dish and how well it predicted it's shape. Also just from a tiny amount of blue it concluded that there should be another pan behind the bottle. That's pretty impressive imo.
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In 1992, Michael Crichton's _Rising Sun_ had manipulation of security camera footage as its crucial plot device. It was painstaking, extraordinarily expensive work, reserved for covering up murder.
With this and several other recent developments, we're reaching a point that it can be done automatically. Which means, cheap or free.
This is why more content needs to be digitally signed. Imagine if you had access to archive.org and ran an AI fact-changer over it. How would you and I know?
The website is hard to use repeatedly and the design is unpleasant to interact with.
I tried erasing people from a random Instagram account's images, and the algorithm does a blurry, but sufficient job of inserting an empty, grey square over all of the faces in the image. I then looked at some examples and realized that the algorithm was supposed to erase the chosen object seamlessly. I'm impressed with the dog example, but the dog example has the benefit of having a dog in the middle of a homogenous texture.
Very interesting, I really like how the objects melt away. I only tried a couple of combinations though because the website doesn't have routing setup properly so when you press back you come back to HN.
With them images I can see I like how they included images that work great (like erasing the dog) and lots of images that show the limitations of the algorithm (like the elephants)
While ugly, I found the UX easy enough to follow on my desktop (I'd hate to see it on a phone / tablet), but then once I made my selections the final image were broken (404s).
If it is possible you should always test these projects on your own images, because you can't be sure if the example images were in the training data set.
You can do that since years ago using Photoshop content-aware thing or Pixelmator repair tool. GIMP and Krita ought to have a similar tool. IIUC the main difference in UX here is that you don't have to select around the object to be removed but merely point at it.
I recently used Pixelmator to remove empty bottles and cigarette butts on skateboarding photos to great effect.
http://deepangel.media.mit.edu/showcase/Works%20terribly
http://deepangel.media.mit.edu/showcase/Another%20greyish%20...
Terrible results in both. It looks like it just works on their examples and doesn't do any better than object detection + paint a solid rectangle on others (arguably, it does worse than that).