When we look at a scene, our visual systems naturally separate objects from each other and from the background.
|Illustration by Robert Cunningham|
Here's another way to think about style: What artists really give us is reality seen through the filter of human perception.
|Illustration by Bernie Fuchs|
This ability to a) distinguish, b) identify, and c) prioritize elements within a scene is called "semantic mapping."
Until recently it has been a distinctly human ability. But machine-learning systems are getting good at it too.
A new scientific paper explains how artificial systems can analyze a photo or video of a scene into its constituent parts and identify each of them, something that would have taken a human a lot of time-consuming work with Photoshop.
This technology will have powerful implications for creating and editing photographic images, but also for interpreting reality into images that seem to have a subjective artistic "style."
Once a computer can semantically map a scene, it can re-render it in any style you want: whether as flat shapes, a line drawing, a caricature, or an impressionist painting.
Here's a video explaining the new tools, which are free to download. (Link to YouTube video: "AI Learns Semantic Image Manipulation")
Scientific Paper: "High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs"