MarioNette: Self-Supervised Sprite Learning

Part of Advances in Neural Information Processing Systems 34 (NeurIPS 2021)

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Dmitriy Smirnov, MICHAEL GHARBI, Matthew Fisher, Vitor Guizilini, Alexei Efros, Justin M. Solomon


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.