  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.
