MarioNette: Self-Supervised Sprite LearningDownload PDF

May 21, 2021 (edited Oct 20, 2021)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: self-supervised learning, dictionary learning, instance segmentation, 2d graphics
  • 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.
  • 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.
  • Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.
  • Code:
14 Replies