  Neural style transfer draws researchers' attention, but the interest focuses
on bitmap images. Various models have been developed for bitmap image
generation both online and offline with arbitrary and pre-trained styles.
However, the style transfer between vector images has not almost been
considered. Our research shows that applying standard content and style losses
insignificantly changes the vector image drawing style because the structure of
vector primitives differs a lot from pixels. To handle this problem, we
introduce new loss functions. We also develop a new method based on
differentiable rasterization that uses these loss functions and can change the
color and shape parameters of the content image corresponding to the drawing of
the style image. Qualitative experiments demonstrate the effectiveness of the
proposed VectorNST method compared with the state-of-the-art neural style
transfer approaches for bitmap images and the only existing approach for
stylizing vector images, DiffVG. Although the proposed model does not achieve
the quality and smoothness of style transfer between bitmap images, we consider
our work an important early step in this area. VectorNST code and demo service
are available at https://github.com/IzhanVarsky/VectorNST.
