Neural Appearance Modeling From Single Images
<|reference_start|>Neural Appearance Modeling From Single Images: We propose a material appearance modeling neural network for visualizing plausible, spatially-varying materials under diverse view and lighting conditions, utilizing only a single photograph of a material under co-located light and view as input for appearance estimation. Our neural architecture is composed of two network stages: a network that infers learned per-pixel neural parameters of a material from a single input photograph, and a network that renders the material utilizing these neural parameters, similar to a BRDF. We train our model on a set of 312,165 synthetic spatially-varying exemplars. Since our method infers learned neural parameters rather than analytical BRDF parameters, our method is capable of encoding anisotropic and global illumination (inter-pixel interaction) information into individual pixel parameters. We demonstrate our model's performance compared to prior work and demonstrate the feasibility of the render network as a BRDF by implementing it into the Mitsuba3 rendering engine. Finally, we briefly discuss the capability of neural parameters to encode global illumination information.<|reference_end|>
@article{idema2024neural,
    title={Neural Appearance Modeling From Single Images},
    author={Jay Idema and Pieter Peers},
    journal={arXiv preprint arXiv:2406.18593},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18593},
    primaryClass={cs.CV cs.GR}
}