A Diffusion Approach to Radiance Field Relighting using Multi-Illumination Synthesis
<|reference_start|>A Diffusion Approach to Radiance Field Relighting using Multi-Illumination Synthesis: Relighting radiance fields is severely underconstrained for multi-view data, which is most often captured under a single illumination condition; It is especially hard for full scenes containing multiple objects. We introduce a method to create relightable radiance fields using such single-illumination data by exploiting priors extracted from 2D image diffusion models. We first fine-tune a 2D diffusion model on a multi-illumination dataset conditioned by light direction, allowing us to augment a single-illumination capture into a realistic -- but possibly inconsistent -- multi-illumination dataset from directly defined light directions. We use this augmented data to create a relightable radiance field represented by 3D Gaussian splats. To allow direct control of light direction for low-frequency lighting, we represent appearance with a multi-layer perceptron parameterized on light direction. To enforce multi-view consistency and overcome inaccuracies we optimize a per-image auxiliary feature vector. We show results on synthetic and real multi-view data under single illumination, demonstrating that our method successfully exploits 2D diffusion model priors to allow realistic 3D relighting for complete scenes. Project site https://repo-sam.inria.fr/fungraph/generative-radiance-field-relighting/<|reference_end|>
@article{poirier-ginter2024a,
    title={A Diffusion Approach to Radiance Field Relighting using
  Multi-Illumination Synthesis},
    author={Yohan Poirier-Ginter, Alban Gauthier, Julien Philip, Jean-Francois
  Lalonde, George Drettakis},
    journal={Computer Graphics Forum, Volume 43 (2024), Number 4},
    year={2024},
    doi={10.1111/cgf.15147},
    archivePrefix={arXiv},
    eprint={2409.08947},
    primaryClass={cs.CV cs.GR}
}