Title Information
Title
Procedural Noise Dataset for "One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns"
Type of Resource (primo)
research_datasets
Abstract
A collection of ~1.3 million procedural noise texture images sampled from 20 noise generating functions. Project page: https://armanmaesumi.github.io/onenoise/ associated publication: "One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns" https://arxiv.org/html/2404.16292v1
Name
Name Part
Maesumi, Arman
Role
Role Term (marcrelator) (authorityURI="http://id.loc.gov/vocabulary/relators", valueURI="http://id.loc.gov/vocabulary/relators/aut")
Author
Name
Name Part
Hu, Dylan
Role
Role Term (marcrelator) (authorityURI="http://id.loc.gov/vocabulary/relators", valueURI="http://id.loc.gov/vocabulary/relators/aut")
Author
Name
Name Part
Saripalli, Krishi
Role
Role Term (marcrelator) (authorityURI="http://id.loc.gov/vocabulary/relators", valueURI="http://id.loc.gov/vocabulary/relators/aut")
Author
Name
Name Part
Kim, Vladimir
Role
Role Term (marcrelator) (authorityURI="http://id.loc.gov/vocabulary/relators", valueURI="http://id.loc.gov/vocabulary/relators/aut")
Author
Name
Name Part
Fisher, Matthew
Role
Role Term (marcrelator) (authorityURI="http://id.loc.gov/vocabulary/relators", valueURI="http://id.loc.gov/vocabulary/relators/aut")
Author
Name
Name Part
Pirk, Sören
Role
Role Term (marcrelator) (authorityURI="http://id.loc.gov/vocabulary/relators", valueURI="http://id.loc.gov/vocabulary/relators/aut")
Author
Name
Name Part
Ritchie, Daniel
Role
Role Term (marcrelator) (authorityURI="http://id.loc.gov/vocabulary/relators", valueURI="http://id.loc.gov/vocabulary/relators/aut")
Author
Origin Information
Date Created
2024
Subject (Local)
Topic
texture synthesis
Subject (Local)
Topic
generative model
Subject (Local)
Topic
diffusion model
Subject (Local)
Topic
procedural noise
Subject (Local)
Topic
Globus
Genre
datasets
Identifier: URI (displayLabel="Globus")
https://app.globus.org/file-manager?origin_id=657d1053-f7d8-4c63-9a4c-326483043ea7&origin_path=%2Fbdrqetf4exu%2F
Note: funding
This material is based upon work that was supported by the National Science Foundation Graduate Research Fellowship under Grant No. 2040433. Part of this work was done while Arman Maesumi was an intern at Adobe Research.
Access Condition: rights statement (href="http://rightsstatements.org/vocab/InC/1.0/")
In Copyright
Access Condition: restriction on access
MIT License
Identifier: DOI
10.26300/a42k-tf07