Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.16292 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911852835897344 |
|---|---|
| author | Maesumi, Arman Hu, Dylan Saripalli, Krishi Kim, Vladimir G. Fisher, Matthew Pirk, Sören Ritchie, Daniel |
| author_facet | Maesumi, Arman Hu, Dylan Saripalli, Krishi Kim, Vladimir G. Fisher, Matthew Pirk, Sören Ritchie, Daniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16292 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns Maesumi, Arman Hu, Dylan Saripalli, Krishi Kim, Vladimir G. Fisher, Matthew Pirk, Sören Ritchie, Daniel Graphics Computer Vision and Pattern Recognition Machine Learning 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. |
| title | One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns |
| topic | Graphics Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2404.16292 |