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Realistic simulation is critical for applications ranging from robotics toanimation. Traditional analytic simulators sometimes struggle to capturesufficiently realistic simulation which can lead to problems including the wellknown "sim-to-real" gap in robotics. Learned simulators have emerged as analternative for better capturing real-world physical dynamics, but requireaccess to privileged ground truth physics information such as precise objectgeometry or particle tracks. Here we propose a method for learning simulatorsdirectly from observations. Visual Particle Dynamics (VPD) jointly learns alatent particle-based representation of 3D scenes, a neural simulator of thelatent particle dynamics, and a renderer that can produce images of the scenefrom arbitrary views. VPD learns end to end from posed RGB-D videos and doesnot require access to privileged information. Unlike existing 2D videoprediction models, we show that VPD's 3D structure enables scene editing andlong-term predictions. These results pave the way for downstream applicationsranging from video editing to robotic planning.

Learning 3D Particle-based Simulators from RGB-D Videos