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Systematic compositionality, or the ability to adapt to novel situations bycreating a mental model of the world using reusable pieces of knowledge,remains a significant challenge in machine learning. While there has beenconsiderable progress in the language domain, efforts towards systematic visualimagination, 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. SVIBoffers a novel framework for a minimal world modeling problem, where models areevaluated based on their ability to generate one-step image-to-imagetransformations under a latent world dynamics. The framework provides benefitssuch as the possibility to jointly optimize for systematic perception andimagination, a range of difficulty levels, and the ability to control thefraction of possible factor combinations used during training. We provide acomprehensive evaluation of various baseline models on SVIB, offering insightinto the current state-of-the-art in systematic visual imagination. We hopethat this benchmark will help advance visual systematic compositionality.

Imagine the Unseen World: A Benchmark for Systematic Generalization in Visual World Models