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arXiv · 2404.16292

One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns

Arman Maesumi·Dylan Hu·Krishi Saripalli·Vladimir G. Kim·Matthew Fisher·Sören Pirk·Daniel Ritchie
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Abstract

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.

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Arman Maesumi, Dylan Hu, Krishi Saripalli, Vladimir G. Kim, Matthew Fisher, Sören Pirk, Daniel Ritchie. 2024-04-25. One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns. https://doi.org/10.1145/3658195

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