AI researchers and Stewart fans were suprised (and pleased) to discover her contribution to the field.
The team used existing neural networks to transfer the style of this painting onto a test frame, and then fine-tuned their setup by adding “blocks of color and texture” until they’d created the desired painting-like effect. When this transfer process was correctly tuned, they applied it different parts of the film, producing frames like the ones below. It’s a simple technique deployed convincingly.
There is of course a bit of light-hearted snobbery here (“Why on Earth is a Hollywood actress getting involved in machine learning?! “), but the fact is that these machine learning tools, once thought of as esoteric and specialized, have become increasingly mainstream. Open source AI frameworks like Tensor Flow and Keras make it easy for anyone to try and implement code, and the commercialization of specifics techniques like style transfer (even Facebook offers style transfer image filters ) pushes this research into popular culture.
Arguably, the AI revolution isn’t just powered by abundant data and GPUs — to truly thrive it also needs an open community and accessible tools. Stewart’s paper is brilliant example of how that works, and what can be achieved.
