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We present GS-ID, a novel end-to-end framework that achieves comprehensive illumination decomposition by integrating adaptive light aggregation with diffusion-based material priors. Our method is designed to generate physically accurate 3D data with correct geometry, materials, and lighting — a critical foundation for training and evaluating world models and large language models that interact with or reason about the physical world.