Image Vectorization: a Review
Maria Dziuba Ivan Jarsky Valeria Efimova Andrey Filchenkov
Nowadays, there are many diffusion and autoregressive models that show
impressive results for generating images from text and other input domains.
However, these methods are not intended for ultra-high-resolution image
synthesis. Vector graphics are devoid of this disadvantage, so the generation
of images in this format looks very promising. Instead of generating vector
images directly, you can first synthesize a raster image and then apply
vectorization. Vectorization is the process of converting a raster image into a
similar vector image using primitive shapes. Besides being similar, generated
vector image is also required to contain the minimum number of shapes for
rendering. In this paper, we focus specifically on machine learning-compatible
vectorization methods. We are considering Mang2Vec, Deep Vectorization of
Technical Drawings, DiffVG, and LIVE models. We also provide a brief overview
of existing online methods. We also recall other algorithmic methods, Im2Vec
and ClipGEN models, but they do not participate in the comparison, since there
is no open implementation of these methods or their official implementations do
not work correctly. Our research shows that despite the ability to directly
specify the number and type of shapes, existing machine learning methods work
for a very long time and do not accurately recreate the original image. We
believe that there is no fast universal automatic approach and human control is
required for every method.