ROGR: Relightable 3DObjects using Generative Relighting

We introduce ROGR, a novel approach that reconstructs a relightable 3D model of an that object simulates captured the ef from fects multiple of placing vie the ws, object driven under by a no generati vel en v vironment e relighting illuminamodel tions. Our method samples the appearance of the object under multiple lighting environments, creating a dataset that is used to train a lighting-conditioned Neural environmental Radiance Field lighting.