W.H. Zhang

Real-to-Sim Generation: Synthesizing Scenario Programs from Real-World Data via Constraint Solving

The demand for synthetic training data is hindered by the sim-to-real gap, as current data-driven and LLM-based generators often produce physically implausible scenarios. To address this, we propose R2SGEN, a Real-to-Sim framework that synthesizes structured scenario programs fro...