AI-Generated Texture Images for Tactile Roughness Evaluation Across Six Materials
by Yui Momiyama·Updated 9d ago
5.2 GB1files
Available on 1 platform
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Description
5.2 GB of texture images generated by a text-to-image AI model for quantitative tactile roughness evaluation. The dataset spans six material categories—wood, metal, stone, fiber, plastic, and ceramic—with roughness levels systematically varied by numerical prompts. Created by Yui Momiyama and last updated in May 2026, it is intended to support reproducibility and research on tactile perception and AI-based material representation.
Use Cases
Training models to predict tactile roughness from visual texture features based on AI-generated images.
Benchmarking generative AI's ability to create perceptually meaningful material textures across categories like wood and metal.
Studying the relationship between quantitative prompt parameters and perceived material properties.
Developing datasets for haptic rendering and virtual material simulation based on visual inputs.
Strengths
Systematic control of a key perceptual variable (roughness) using quantitative numerical prompts.
Covers six distinct material categories, providing a degree of categorical diversity.
Includes generated images, category labels, roughness levels, and prompt metadata to support reproducibility.
Dataset size of 5.2 GB suggests a substantial collection of high-resolution images.
Limitations
Row count and specific image dimensions are unknown, limiting precise suitability assessment.
Column-level documentation is absent; field semantics must be inferred after download.
The dataset's realism and perceptual alignment are inherently limited by the generative AI model used.
Provenance
Source
Yui Momiyama via figshare.
Collection Method
Images generated using a text-to-image generative AI model with quantitative prompt expressions.
Freshness
Last updated 2026-05-28 13:57:28; freshness should be verified.
License is CC-BY-4.0. Data is packaged in a 5.2 GB ZIP file.