ROGR: Relightable 3D Objects using Generative Relighting
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917123515744256 |
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| author | Tang, Jiapeng Levine, Matthew Verbin, Dor Garbin, Stephan J. Nießner, Matthias Brualla, Ricardo Martin Srinivasan, Pratul P. Henzler, Philipp |
| author_facet | Tang, Jiapeng Levine, Matthew Verbin, Dor Garbin, Stephan J. Nießner, Matthias Brualla, Ricardo Martin Srinivasan, Pratul P. Henzler, Philipp |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03163 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ROGR: Relightable 3D Objects using Generative Relighting Tang, Jiapeng Levine, Matthew Verbin, Dor Garbin, Stephan J. Nießner, Matthias Brualla, Ricardo Martin Srinivasan, Pratul P. Henzler, Philipp Computer Vision and Pattern Recognition Graphics 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. |
| title | ROGR: Relightable 3D Objects using Generative Relighting |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2510.03163 |