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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 from real-world data. T
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