# Conditional Vector Graphics Generation for Music Cover Images

arXiv: https://arxiv.org/abs/2205.07301 · HTML: https://mlsanity.com/paper/2205.07301/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 54 references that are in the map.
- First posted on arXiv 15 May 2022.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Conditional Image Generation with PixelCNN Decoders](https://mlsanity.com/paper/1606.05328/) (2016)
- [AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks](https://mlsanity.com/paper/1711.10485/) (2017)
- [NVAE: A Deep Hierarchical Variational Autoencoder](https://mlsanity.com/paper/2007.03898/) (2020)
- [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks](https://mlsanity.com/paper/1612.03242/) (2016)
- [Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models](https://mlsanity.com/paper/2103.04922/) (2021)
- [CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders](https://mlsanity.com/paper/2106.14843/) (2021)
- [MirrorGAN: Learning Text-to-image Generation by Redescription](https://mlsanity.com/paper/1903.05854/) (2019)
- [Listen to Look: Action Recognition by Previewing Audio](https://mlsanity.com/paper/1912.04487/) (2019)
- [DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation](https://mlsanity.com/paper/2007.11301/) (2020)
- [Multiple Sound Sources Localization from Coarse to Fine](https://mlsanity.com/paper/2007.06355/) (2020)
- [Object-driven Text-to-Image Synthesis via Adversarial Training](https://mlsanity.com/paper/1902.10740/) (2019)
- [Im2Vec: Synthesizing Vector Graphics without Vector Supervision](https://mlsanity.com/paper/2102.02798/) (2021)
- [A Learned Representation for Scalable Vector Graphics](https://mlsanity.com/paper/1904.02632/) (2019)
- [Speech2Face: Learning the Face Behind a Voice](https://mlsanity.com/paper/1905.09773/) (2019)
- [Wav2Pix: Speech-conditioned Face Generation using Generative Adversarial Networks](https://mlsanity.com/paper/1903.10195/) (2019)
- [Discovering Pattern Structure Using Differentiable Compositing](https://mlsanity.com/paper/2010.08788/) (2020)
- [A Tutorial on Deep Learning for Music Information Retrieval](https://mlsanity.com/paper/1709.04396/) (2017)
- [Feature Learning for Chord Recognition: The Deep Chroma Extractor](https://mlsanity.com/paper/1612.05065/) (2016)

## Built on by (most cited first, 5 of 5)

- [Image Vectorization: a Review](https://mlsanity.com/paper/2306.06441/) (2023)
- [Symbolic Graphics Programming with Large Language Models](https://mlsanity.com/paper/2509.05208/) (2025)
- [Neural Style Transfer for Vector Graphics](https://mlsanity.com/paper/2303.03405/) (2023)
- [Leveraging Large Language Models For Scalable Vector Graphics Processing: A Review](https://mlsanity.com/paper/2503.04983/) (2025)
- [ICG: Improving Cover Image Generation via MLLM-based Prompting and Personalized Preference Alignment](https://mlsanity.com/paper/2605.27374/) (2026)

## Abstract (arXiv)

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.