Light the Sprite: Pixel Art Dynamic Light Map Generation

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dc.contributor.authorNikolov, Ivanen_US
dc.contributor.editorCeylan, Duyguen_US
dc.contributor.editorLi, Tzu-Maoen_US
dc.date.accessioned2025-05-09T09:35:11Z
dc.date.available2025-05-09T09:35:11Z
dc.date.issued2025
dc.description.abstractCorrect lighting and shading are vital for pixel art design. Automating texture generation, such as normal, depth, and occlusion maps, has been a long-standing focus. We extend this by proposing a deep learning model that generates point and directional light maps from RGB pixel art sprites and specified light vectors. Our approach modifies a UNet architecture with CIN layers to incorporate positional and directional information, using ZoeDepth for training depth data. Testing on a popular pixel art dataset shows that the generated light maps closely match those from depth or normal maps, as well as from manual programs. The model effectively relights complex sprites across styles and functions in real time, enhancing artist workflows. The code and dataset are here - https://github.com/IvanNik17/light-sprite.en_US
dc.description.sectionheadersShort Paper 1
dc.description.seriesinformationEurographics 2025 - Short Papers
dc.identifier.doi10.2312/egs.20251032
dc.identifier.isbn978-3-03868-268-4
dc.identifier.issn1017-4656
dc.identifier.pages4 pages
dc.identifier.urihttps://doi.org/10.2312/egs.20251032
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/egs20251032
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Non-photorealistic rendering; Image-based rendering; Theory of computation → Machine learning theory
dc.subjectComputing methodologies → Non
dc.subjectphotorealistic rendering
dc.subjectImage
dc.subjectbased rendering
dc.subjectTheory of computation → Machine learning theory
dc.titleLight the Sprite: Pixel Art Dynamic Light Map Generationen_US
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