For the fact that we're always ready to hand over our photos for the sake of a trend, the internet's current obsession is an AI portrait generator that deconstructs your selfies and rebuilds them as Renaissance and Baroque portraits.
Created by
researchers at the MIT-IBM Watson AI Lab, AI Portrait Ars is a fun way to see how you would have been perceived if you lived in another time period.
"Portraits interpret the external beauty, social status, and then go beyond our body and face," its creators wrote in the site's "Why" section. "A portrait becomes a psychological analysis and a deep reflection on our existence."
Unless, apparently, you're not white.
When I uploaded my photo into AI Portrait Ars, I was both horrified and amused by what the tool spat out. The painted version of me had my dark circles and eyebrows — at least my morning makeup routine is pulling through — but it whitened my skin to an unearthly pale tone, turned my flat nose into one with a prominent bridge and pointed end, and replaced my very hooded eyes with heavily lidded ones.
We won't even talk about the atrocity that was supposed to be my lips.
The researchers wrote that the AI pulls from a data set of "tens of thousands of paintings from the Early Renaissance to Contemporary Art" using Generative Adversarial Network (GAN) models to produce "different styles and levels of abstraction."
There are two neural networks at play here: a discriminator, which figures out what makes a face and can recognize portraits, and a generator, which paints the portraits. Unlike neural style transfers, which take images and alter them with unique colors and textures while preserving the original's features, the AI Portrait Ars "paintings" completely redesign the original subject. In an example provided by the site, the creators say that the model settled on a "Renaissance style" and recreated its (white) subject by "highlighting the elegance of the aquiline nose, the smoothness of the forehead."
"This type of portraiture is quite distinctive of the Western artistic tradition," the creators continued on the site's "Why" section. "Training our models on a data set with such a strong bias leads us to reflect on the importance of AI fairness."
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