  Generative Adversarial Networks (GAN) have motivated a rapid growth of the
domain of computer image synthesis. As almost all the existing image synthesis
algorithms consider an image as a pixel matrix, the high-resolution image
synthesis is complicated.A good alternative can be vector images. However, they
belong to the highly sophisticated parametric space, which is a restriction for
solving the task of synthesizing vector graphics by GANs. In this paper, we
consider a specific application domain that softens this restriction
dramatically allowing the usage of vector image synthesis.
  Music cover images should meet the requirements of Internet streaming
services and printing standards, which imply high resolution of graphic
materials without any additional requirements on the content of such images.
Existing music cover image generation services do not analyze tracks
themselves; however, some services mostly consider only genre tags. To generate
music covers as vector images that reflect the music and consist of simple
geometric objects, we suggest a GAN-based algorithm called CoverGAN. The
assessment of resulting images is based on their correspondence to the music
compared with AttnGAN and DALL-E text-to-image generation according to title or
lyrics. Moreover, the significance of the patterns found by CoverGAN has been
evaluated in terms of the correspondence of the generated cover images to the
musical tracks. Listeners evaluate the music covers generated by the proposed
algorithm as quite satisfactory and corresponding to the tracks. Music cover
images generation code and demo are available at
https://github.com/IzhanVarsky/CoverGAN.
