  Systematic compositionality, or the ability to adapt to novel situations by
creating a mental model of the world using reusable pieces of knowledge,
remains a significant challenge in machine learning. While there has been
considerable progress in the language domain, efforts towards systematic visual
imagination, or envisioning the dynamical implications of a visual observation,
are in their infancy. We introduce the Systematic Visual Imagination Benchmark
(SVIB), the first benchmark designed to address this problem head-on. SVIB
offers a novel framework for a minimal world modeling problem, where models are
evaluated based on their ability to generate one-step image-to-image
transformations under a latent world dynamics. The framework provides benefits
such as the possibility to jointly optimize for systematic perception and
imagination, a range of difficulty levels, and the ability to control the
fraction of possible factor combinations used during training. We provide a
comprehensive evaluation of various baseline models on SVIB, offering insight
into the current state-of-the-art in systematic visual imagination. We hope
that this benchmark will help advance visual systematic compositionality.
