Make Your Own Sprites: Aliasing-Aware and Cell-Controllable Pixelization

Zongwei Wu, Liangyu Chai, Nanxuan Zhao, Bailin Deng, Yongtuo Liu, Qiang Wen, Junle Wang, Shengfeng He

Research output: Contribution to journalArticlepeer-review

1 Citation (SciVal)

Abstract

Pixel art is a unique art style with the appearance of low resolution images. In this paper, we propose a data-drivenpixelization method that can produce sharp and crisp cell effects with controllable cell sizes. Our approach overcomes the limitation of existing learning-based methods in cell size control by introducing a reference pixel art to explicitly regularize the cell structure. In particular, the cell structure features of the reference pixel art are used as an auxiliary input for the pixelization process, and for measuring the style similarity between the generated result and the reference pixel art. Furthermore, we disentangle the pixelization process into specific cell-aware and aliasing-aware stages, mitigating the ambiguities in joint learning of cell size, aliasing effect, and color assignment. To train our model, we construct a dedicated pixel art dataset and augment it with different cell sizes and different degrees of anti-aliasing effects. Extensive experiments demonstrate its superior performance over state-of-the-arts in terms of cell sharpness and perceptual expressiveness. We also show promising results of video game pixelization for the first time. Code and dataset are available at https://github.com/WuZongWei6/Pixelization.

Original languageEnglish
Article number193
JournalACM Transactions on Graphics
Volume41
Issue number6
Early online date30 Nov 2022
DOIs
Publication statusPublished - 31 Dec 2022

Bibliographical note

Publisher Copyright:
© 2022 ACM.

Keywords

  • generative adversarial networks
  • image-to-image translation
  • pixelization

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design

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