{"ID":2861046,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.03163","arxiv_id":"2510.03163","title":"ROGR: Relightable 3D Objects using Generative Relighting","abstract":"We introduce ROGR, a novel approach that reconstructs a relightable 3D model of an object captured from multiple views, driven by a generative relighting model that simulates the effects of placing the object under novel environment illuminations. Our method samples the appearance of the object under multiple lighting environments, creating a dataset that is used to train a lighting-conditioned Neural Radiance Field (NeRF) that outputs the object's appearance under any input environmental lighting. The lighting-conditioned NeRF uses a novel dual-branch architecture to encode the general lighting effects and specularities separately. The optimized lighting-conditioned NeRF enables efficient feed-forward relighting under arbitrary environment maps without requiring per-illumination optimization or light transport simulation. We evaluate our approach on the established TensoIR and Stanford-ORB datasets, where it improves upon the state-of-the-art on most metrics, and showcase our approach on real-world object captures.","short_abstract":"We introduce ROGR, a novel approach that reconstructs a relightable 3D model of an object captured from multiple views, driven by a generative relighting model that simulates the effects of placing the object under novel environment illuminations. Our method samples the appearance of the object under multiple lighting...","url_abs":"https://arxiv.org/abs/2510.03163","url_pdf":"https://arxiv.org/pdf/2510.03163v3","authors":"[\"Jiapeng Tang\",\"Matthew Levine\",\"Dor Verbin\",\"Stephan J. Garbin\",\"Matthias Nießner\",\"Ricardo Martin Brualla\",\"Pratul P. Srinivasan\",\"Philipp Henzler\"]","published":"2025-10-03T16:35:22Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.GR\"]","methods":"[]","has_code":false}