Kavli Affiliate: Matthew Fisher
| First 5 Authors: Dmitriy Smirnov, Michael Gharbi, Matthew Fisher, Vitor Guizilini, Alexei A. Efros
| Summary:
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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