MarioNette: Self-Supervised Sprite Learning

Dmitriy Smirnov, MICHAEL GHARBI, Matthew Fisher, Vitor Campagnolo Guizilini, Alexei A Efros, Justin Solomon

Keywords: self-supervised learning, dictionary learning, instance segmentation, 2d graphics

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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TL;DR: We jointly learn a dictionary of texture patches and train a network that places them onto a canvas, effectively deconstructing sprite-based content video content.

Code: http://github.com/dmsm/MarioNette