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).
MIT Media Lab has much better PR, of course.
Deep Angel is based on an architecture combining Mask R-CNN (for object detection and instance segementation) and DeepFill for image inpainting.
And here's the paper behind DeepFill
If you look at the papers, you'll see which one has the best inpainting results.
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.
This is what Deep Angel makes of it: https://i.imgur.com/JPvq7P7.png
This is what Krita makes of it, after I manually erase the bottle and drag the smart patch tool over it: https://i.imgur.com/nGa5e0L.png
Deep Angel is better, but it's underwhelming.
So turns objects into ghost versions
It does better with this dog: https://i.imgur.com/mkP5tnb.png → https://i.imgur.com/3F7pPj0.png
If it can't seamlessly handle a picture where the entire replacement is a single texture, then... nah, I'm not impressed.
"the first axiom of spam: if you don't see spam around you, that means that everything around you is spam. enjoy the apophatic palimpsests."
But I don't understand what it means.
Text on website (un-zalgofied) says
The Kendall Corporation has been providing infrastructure for worship since 1964. With over a hundred funded fads, religions and spiritual movements across the five continents, Kendall Corporation is the leading powerhouse for seeding creeds.
1. Do you have an early-stage development of a conducive mesostructure?
2. Are you good at building platforms to enlighten, seduce and draw crowds in?
3. Is a network of your making growing at superlinear speed?
Please reach out to us; we may be able to provide angel funding for your startup idea.
(removed address and phone number -- even though fake, I don't like linking people's info)
Looks like an MIT Media Lab graduate's painfully pretentious arg/art project (i love it) :D
Then it leads here: http://isthisabook.club/
With this and several other recent developments, we're reaching a point that it can be done automatically. Which means, cheap or free.
See PatchMatch algorithm.
Paper here: http://gfx.cs.princeton.edu/pubs/Barnes_2009_PAR/patchmatch....
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.
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)
I recently used Pixelmator to remove empty bottles and cigarette butts on skateboarding photos to great effect.