Publication Type

Journal Article

Version

publishedVersion

Publication Date

12-2022

Abstract

Pixel art is a unique art style with the appearance of low resolution images. In this paper, we propose a data-driven pixelization 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 cellaware 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.

Keywords

Pixelization, Generative Adversarial Networks, Image-to-Image Translation

Discipline

Graphics and Human Computer Interfaces

Research Areas

Software and Cyber-Physical Systems

Publication

ACM Transactions on Graphics

Volume

41

Issue

6

First Page

1

Last Page

16

ISSN

0730-0301

Identifier

10.1145/3550454.3555482

Publisher

ACM

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3550454.3555482

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