  We present GATSBI, a generative model that can transform a sequence of raw
observations into a structured latent representation that fully captures the
spatio-temporal context of the agent's actions. In vision-based decision-making
scenarios, an agent faces complex high-dimensional observations where multiple
entities interact with each other. The agent requires a good scene
representation of the visual observation that discerns essential components and
consistently propagates along the time horizon. Our method, GATSBI, utilizes
unsupervised object-centric scene representation learning to separate an active
agent, static background, and passive objects. GATSBI then models the
interactions reflecting the causal relationships among decomposed entities and
predicts physically plausible future states. Our model generalizes to a variety
of environments where different types of robots and objects dynamically
interact with each other. We show GATSBI achieves superior performance on scene
decomposition and video prediction compared to its state-of-the-art
counterparts.
