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This does look like an interesting step forward however I’ve found the biggest limitations of previous techniques to be

- people who are using occupying a wider z-axis (for example leaning forwards in the camera or who have arms in front of them)

- people holding objects like cups

How well do your method handle those kind of situations?

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I'm not sure how either of those situations would trip up the system they're using. For a system trained on the background image, what difference does it make if the subject is holding a cup? The cup is not the background image, and it would be obvious in the same way that it's obvious the subject isn't the background image.

He might be referring to transparent/translucent/refractive objects, like a glass cup. Supposedly this technique can manage the transparency, but not refraction (and maybe the refraction could trip the transparency into failing).

I'm not associated with the paper, but I don't think this will have the same kinds of effects. It's effectively using a photo without the user to discriminate the background from any subjects in the scene.

The neural network used seems mostly for allowing for variations in lighting and dealing with the fuzzy effects you'll usually get around the subject. Depth of the subject doesn't appear to be relevant here.

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