title: MarioNette: Self-Supervised Sprite Learning
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. 
authors: Dmitriy Smirnov MICHAEL GHARBI Matthew Fisher Vitor Campagnolo Guizilini Alexei A Efros Justin Solomon
hIndex: Alexei A Efros 89