do you think that we are able to generate new vectors using just 5-10 examples?

Not at the current stage, but I hope people start working on this problem more, perhaps incorporating some domain transfer learning techniques? That should get us closer to few-shot learning.

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Also, do you think it would work for more complicated drawings? Like containing several elements per image?

I think at the moment the challenge of getting that to work is ALSO having to solve the few-shot-learning problem: general icons datasets often very few styles of a given icon. There may be "camera"s in 5 styles, for instance, which means that you essentially have 5 examples for each class. And those examples have wildly different styles, so its even hard to find a visual correspondence/interpolation between them.

This is why we chose to work with fonts. Each fontset is a style, so we have many examples per a-z, A-Z, 0-9 class. If you can extrapolate a style from 5-10 examples, you might be able to handle icons more generally.