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arXiv · 2104.14553

MarioNette: Self-Supervised Sprite Learning

Dmitriy Smirnov·Michael Gharbi·Matthew Fisher·Vitor Guizilini·Alexei A. Efros·Justin Solomon
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Abstract

Artists and video game designers often construct 2D animations using libraries of sprites -- textured patches of objects and characters. We propose a deep learning approach that decomposes sprite-based video animations into a disentangled representation of recurring graphic elements in a self-supervised manner. By jointly learning a dictionary of possibly transparent patches and training a network that places them onto a canvas, we deconstruct sprite-based content into a sparse, consistent, and explicit representation that can be easily used in downstream tasks, like editing or analysis. Our framework offers a promising approach for discovering recurring visual patterns in image collections without supervision.

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Dmitriy Smirnov, Michael Gharbi, Matthew Fisher, Vitor Guizilini, Alexei A. Efros, Justin Solomon. 2021-04-29. MarioNette: Self-Supervised Sprite Learning. https://arxiv.org/abs/2104.14553

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