Rasterizing and antialiasing vector line art in the pixel art style

Inglis, Tiffany C.; Vogel, Daniel; Kaplan, Craig S.

doi:10.1145/2486042.2486044

Cited by 12 publications

(9 citation statements)

References 10 publications

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“…There are methods for vectorizing pixel art [Kopf and Lischinski 2011] and converting vectored art to pixels [Inglis et al 2013]. These could provide insights into appropriate ways to convert 3D models to these more extreme abstractions.…”

Section: Discussionmentioning

confidence: 99%

Abstract depiction of human and animal figures

Dodgson

2018

Proceedings of the Joint Symposium on Computational Aesthetics and Sketch-Based Interfaces and Modeling and Non-Photorealistic

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Section: Discussionmentioning

confidence: 99%

Abstract depiction of human and animal figures

Dodgson

2018

Proceedings of the Joint Symposium on Computational Aesthetics and Sketch-Based Interfaces and Modeling and Non-Photorealistic

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“…(iii) Applying a state-of-the-art vector line rasterization algorithm designed for pixel art, called Superpixelator [IVK13], to l n m (1 ≤ m ≤ M). Since the dominant factor to the quality of our results is the feature-aware optimization (Section 4), we evaluate its performance via a comparison with three alternative rasterization algorithms as follows.…”

Section: Discussionmentioning

confidence: 99%

“…Antialiasing techniques were introduced to make the rasterized shapes visually smoothing [Wu91]. This method is further improved in [IVK13] by preserving symmetry and adopting manual antialiasing. For example, Gerstner et al [GDA * 12] converted an input image to a pixelated output image by jointly optimizing a mapping of features and a reduced color palette.…”

Section: Related Workmentioning

confidence: 99%

“…Inglis and Kaplan [IK12] proposed a novel algorithm to pixelate vector line art where artifacts, such as jaggies and broken lines, are deliberately handled to fulfill the style of pixel art. This method is further improved in [IVK13] by preserving symmetry and adopting manual antialiasing. Recent work also investigated how to fabricate pixelated shape using physical building blocks [KLC*15].…”

Section: Related Workmentioning

confidence: 99%

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Feature‐Aware Pixel Art Animation

Kuo

Yang

Chu

2016

Computer Graphics Forum

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Pixel art is a modern digital art in which high resolution images are abstracted into low resolution pixelated outputs using concise outlines and reduced color palettes. Creating pixel art is a labor intensive and skill-demanding process due to the challenge of using limited pixels to represent complicated shapes. Not surprisingly, generating pixel art animation is even harder given the additional constraints imposed in the temporal domain. Although many powerful editors have been designed to facilitate the creation of still pixel art images, the extension to pixel art animation remains an unexplored direction. Existing systems typically request users to craft individual pixels frame by frame, which is a tedious and error-prone process. In this work, we present a novel animation framework tailored to pixel art images. Our system bases on conventional key-frame animation framework and state-of-the-art image warping techniques to generate an initial animation sequence. The system then jointly optimizes the prominent feature lines of individual frames respecting three metrics that capture the quality of the animation sequence in both spatial and temporal domains. We demonstrate our system by generating visually pleasing animations on a variety of pixel art images, which would otherwise be difficult by applying state-of-the-art techniques due to severe artifacts.

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“…Nonograms need a coarse pixel image which can be derived from smooth vector art by rasterization. Beyond the classic research by Bresenham, more recent research focuses on generating pixel art [GDA * 13, IVK13] or representing polygonal shapes onto the grid [BKvK * 16] with bounded error. An interesting link exists with computer tomography, where one also gets information on cross-sections and the objective is to reconstruct the model [BK04].…”

Section: Related Workmentioning

confidence: 99%

Design and Automated Generation of Japanese Picture Puzzles

Kerkhof

Jong²,

Parment³

et al. 2019

Computer Graphics Forum

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We introduce the generalized nonogram, an extension of the well‐known nonogram or Japanese picture puzzle. It is not based on a regular square grid but on a subdivision (arrangement) with differently shaped cells, bounded by straight lines or curves. To generate a good, clear puzzle from a filled line drawing, the arrangement that is formed for the puzzle must meet a number of criteria. Some of these relate to the puzzle and some to the geometry. We give an overview of these criteria and show that a puzzle can be generated by an optimization method like simulated annealing. Experimentally, we analyze the convergence of the method and the remaining penalty score on several input pictures along with various other design options.

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Rasterizing and antialiasing vector line art in the pixel art style

Cited by 12 publications

References 10 publications

Abstract depiction of human and animal figures

Abstract depiction of human and animal figures

Feature‐Aware Pixel Art Animation

Design and Automated Generation of Japanese Picture Puzzles

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