> So basically, if you have impossible to get input data, this network can do its magic.

Except they evidently got the input data for the examples in the paper, so it can't be impossible to get.

They cite at least two different methods for adding depth information to a single image to generate the necessary RGBD data, different views of which can then be rendered with their inpainting applied:

- https://arxiv.org/abs/1907.01341

- https://research.cs.cornell.edu/megadepth/

Yes, you can always create that data by hand, but it's too expensive and, hence, impossible to scale.

As for using other AIs, they tend to not work too well on more complex images.

But in any case, getting the data that you need to be able to use the paper here is very challenging.

Edit: I should probably say that I have hands-on experience with MegaDepth and Midas and that it was underwhelming. Both of them assume a Gradient from close to far from the bottom to the top and both of them assume that optical variation will be in the foreground. A photo of a dining table from the side is already enough to confuse both of them.

True but their collab demo uses a separate network to infer depth information from a simple RGB picture (its a classical task nowadays) so it is not a problem in practice.

Sadly not, it's still an unsolved problem in the general case. Yes, it works tolerably well for some images, but converting RGB to RGBD is anything but easy. That's why the 3D-ification of cinema movies still requires thousands of people and millions in budget.

True, we get results good enough for that usage but not for general applications.

But it is a problem well studied in machine learning and for which you can get off-the-shelf networks to use in conjunction with this paper.