mlsanityData to 7 Oct 2026

Pixel VQ-VAEs for Improved Pixel Art Representation

arXiv 2203.12130 ↗First posted 23 Mar 2022cs.CV cs.LGcited by 4

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Abstract (arXiv)

Machine learning has had a great deal of success in image processing. However, the focus of this work has largely been on realistic images, ignoring more niche art styles such as pixel art. Additionally, many traditional machine learning models that focus on groups of pixels do not work well with pixel art, where individual pixels are important. We propose the Pixel VQ-VAE, a specialized VQ-VAE model that learns representations of pixel art. We show that it outperforms other models in both the quality of embeddings as well as performance on downstream tasks.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 20 of its 33 references that are in the map. First posted on arXiv 23 Mar 2022.

Builds on

20 of 33 references.

References · 20

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8 more
  1. 2019Controllable Text-to-Image Generationcited by 187
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Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 1.

4 papers.

  1. 2023The Five-Dollar Model: Generating Game Maps and Sprites from Sentence Embeddingscited by 14
  2. 2022SketchBetween: Video-to-Video Synthesis for Sprite Animation via Sketchescited by 6
  3. 2022Clustering-based Tile Embedding (CTE): A General Representation for Level Design with Skewed Tile Distributionscited by 1
  4. 2024A Missing Data Imputation GAN for Character Sprite Generationcited by 0