Make your own sprites: Aliasing-aware and cell-controllable pixelization

Wu, Zongwei, Chai, Liangyu, Zhao, Nanxuan, Deng, Bailin ORCID: https://orcid.org/0000-0002-0158-7670, Liu, Yongtuo, Wen, Qiang, Wang, Junle and He, Shengfeng 2022. Make your own sprites: Aliasing-aware and cell-controllable pixelization. ACM Transactions on Graphics 41 (6) , 193. 10.1145/3550454.3555482

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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 pro- duce 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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Publisher:	Association for Computing Machinery (ACM)
ISSN:	0730-0301
Date of First Compliant Deposit:	24 September 2022
Date of Acceptance:	14 September 2022
Last Modified:	05 Nov 2024 17:15
URI:	https://orca.cardiff.ac.uk/id/eprint/152816

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