Implicit Neural Representation of Tileable Material Textures
<|reference_start|>Implicit Neural Representation of Tileable Material Textures: We explore sinusoidal neural networks to represent periodic tileable textures. Our approach leverages the Fourier series by initializing the first layer of a sinusoidal neural network with integer frequencies with a period $P$. We prove that the compositions of sinusoidal layers generate only integer frequencies with period $P$. As a result, our network learns a continuous representation of a periodic pattern, enabling direct evaluation at any spatial coordinate without the need for interpolation. To enforce the resulting pattern to be tileable, we add a regularization term, based on the Poisson equation, to the loss function. Our proposed neural implicit representation is compact and enables efficient reconstruction of high-resolution textures with high visual fidelity and sharpness across multiple levels of detail. We present applications of our approach in the domain of anti-aliased surface.<|reference_end|>
@article{paz2024implicit,
    title={Implicit Neural Representation of Tileable Material Textures},
    author={Hallison Paz, Tiago Novello, Luiz Velho},
    journal={arXiv preprint arXiv:2402.02208},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.02208},
    primaryClass={cs.CV cs.GR cs.LG}
}