Vera: Decoupled Simulation for Efficient and Physically-Grounded World Modeling

World models have recently gained significant popularity, largely in the form of video-generation models which are able to generate high-quality diverse videos of various scenes. Due to these abilities, many have tried to apply them to the task of generating synthetic data or planning for robotics applications. However, these trials have so far been limited by the lack of physical accuracy in generated videos. Because these video models are trained purely to predict future video frames, they struggle to learn the underlying 3D dynamics and cues required to generate physically consistent and accurate videos. In this work, we propose Vera, a decoupled approach to world modeling. By splitting the task into two stages, simulation and rendering, and carefully designing the simulation representation and architecture, we are able to train a lightweight simulation model which can learn generalizable physics and can be used for robotics planning in real-time. This model outputs 3D trajectories, allowing us to leverage pre-trained models for high-quality video generation through motion conditioning. Across multiple benchmarks, we demonstrate Vera's ability to simulate complex dynamics, learn generalizable physics laws, and generate high-quality, physically-accurate videos.