Creating Variation When Building Image Generation Datasets

Year: 2025 Authors: Jhovani Gallardo Moreno; Omar Khan; Michael Wehar

Core claim

Adding controllable parameters to drawing algorithms enables hundreds or thousands of related images, making algorithmic artworks more reusable and useful.

Topics

image datasets, parameterized drawing algorithms, visual style, algorithmic art, variation

Domains

computer science, algorithm design, randomization, parameterization, generative art, digital clothing design, print media, visual design

Methods

parameter sampling, dataset builder, web-based review, step-by-step animation

Media

JavaScript, 2D canvas, line drawings, basic shapes

Source status

This page publishes metadata and extracted analytical signals only. Raw PDF and full OCR text are kept local for now.