Undress paintings using Machine Learning

A little-known application of artificial intelligence (so far) is to analyze a pictorial language. When a neural network process images, it analyzes shapes, colors, directions, textures and compositions. It is a way of stripping a language of all symbolic references.

A network can be trained to do exactly the opposite, and find objects, people, or whatever, on a image, but that is out of the scope of this experiment.

To analyze images in purely pictorial terms, stripped of all symbolism, a StyleGan2 neural network has been trained on a set of about 300 images of my paintings. These paintings have been specially selected looking for a certain visual continuity, so I have focused on examples from 2017, 2018 and 2019.

After processing the images, we obtain a model, that is capable of reproducing infinite variants, all of them free of symbolic value.


The cracks pattern comes from the not painted green lines around objects.

El perro, Juanma Moreno Sánchez
El perro, Juanma Moreno Sánchez

Emerald green and fluorescent pink

The use of this range of greens, and the occasional use of fluorescent pink has been common in my career since more or less 2014.

Juanma Moreno Sánchez 2014
Otro puto selfie, Juanma Moreno Sánchez

Leather and fabric


Botellón en el parking, Juanma Moreno Sánchez
Botellón en el parking, Juanma Moreno Sánchez

Simplified landscape

In my paintings, the characters usually appear outdoors, surrounded by vegetation. The landscape does not usually participate in the scene, and therefore it is usually limited to simply establishing a horizontal line that creates an illusion of space..

Dub, Juanma Moreno Sánchez
Dub, Juanma Moreno Sánchez