  It seems easy to imagine a photograph of the Eiffel Tower painted in the
style of Vincent van Gogh's 'The Starry Night', but upon introspection it is
difficult to precisely define what this would entail. What visual elements must
an image contain to represent the 'content' of the Eiffel Tower? What visual
elements of 'The Starry Night' are caused by van Gogh's 'style' rather than his
decision to depict a village under the night sky? Precisely defining 'content'
and 'style' is a central challenge of designing algorithms for artistic style
transfer, algorithms which can recreate photographs using an artwork's style.
My efforts defining these terms, and designing style transfer algorithms
themselves, are the focus of this thesis. I will begin by proposing novel
definitions of style and content based on optimal transport and
self-similarity, and demonstrating how a style transfer algorithm based on
these definitions generates outputs with improved visual quality. Then I will
describe how the traditional texture-based definition of style can be expanded
to include elements of geometry and proportion by jointly optimizing a
keypoint-guided deformation field alongside the stylized output's pixels.
Finally I will describe a framework inspired by both modern neural style
transfer algorithms and traditional patch-based synthesis approaches which is
fast, general, and offers state-of-the-art visual quality.
