[thenomad]

Aha. I was wondering about that.

I've done some experimentation with neural network-based style transfer (this one: https://github.com/jcjohnson/neural-style_), and the results that I got pointed strongly to the same effect: it works well if the two images (source for style and source for content) are very similar in framing, composition and subject, and very badly if they're wildly different.

Having said that, this algorithm seems to be MUCH better than the one I tried at transferring style. I'd have expected those paintings to transfer to the doodles much worse than they did.

But don't expect to take a portrait doodle and a landscape source and have it come out well :)

[TheOtherHobbes]

The "Semantic" tag is misleading, because human perception parses lighting and textures cues in 2D images as 3D hinting.

Representational art is all about modelling, highlighting and/or transforming the hinting, depending on the level of abstraction. E.g. if you look at portraits, the pen/brush strokes usually emphasise 3D structures.

This code does a little of that, but the model is extremely crude compared to the models the human brain uses.

For genuine semantic perception you'd have to duplicate - and maybe improve - the human model. I doubt you can do that in 2D, because the human model is trained by years of genuine 3D perception.

That's not to sound negative - I think this is very impressive visually. But it could be taken further.

[nuclai]

> This code does a little of that.

Actually, the code does none of that ;-) All of the semantics are provided by the users: either as manual annotations or by plugging in an existing architecture for semantic segmentation / pixel labeling. It's designed to be independent of the source of the semantic maps, so we can continue to work on both problems separately.

It works for basic color segmentation already, and here are some of the papers we're integrating currently: http://gitxiv.com/search/?q=segmentation