One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns
<|reference_start|>One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns: Procedural noise is a fundamental component of computer graphics pipelines, offering a flexible way to generate textures that exhibit "natural" random variation. Many different types of noise exist, each produced by a separate algorithm. In this paper, we present a single generative model which can learn to generate multiple types of noise as well as blend between them. In addition, it is capable of producing spatially-varying noise blends despite not having access to such data for training. These features are enabled by training a denoising diffusion model using a novel combination of data augmentation and network conditioning techniques. Like procedural noise generators, the model's behavior is controllable via interpretable parameters and a source of randomness. We use our model to produce a variety of visually compelling noise textures. We also present an application of our model to improving inverse procedural material design; using our model in place of fixed-type noise nodes in a procedural material graph results in higher-fidelity material reconstructions without needing to know the type of noise in advance.<|reference_end|>
@article{maesumi2024one,
    title={One Noise to Rule Them All: Learning a Unified Model of
  Spatially-Varying Noise Patterns},
    author={Arman Maesumi, Dylan Hu, Krishi Saripalli, Vladimir G. Kim, Matthew
  Fisher, S"oren Pirk, Daniel Ritchie},
    journal={arXiv preprint arXiv:2404.16292},
    year={2024},
    doi={10.1145/3658195},
    archivePrefix={arXiv},
    eprint={2404.16292},
    primaryClass={cs.GR cs.CV cs.LG}
}