Moonworks Lunara Aesthetic Dataset

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Authors: Yan Wang, M M Sayeef Abdullah, Partho Hassan, Sabit Hassan
Cited by: 0

Abstract

The dataset spans diverse artistic styles, including regionally grounded aesthetics from the Middle East, Northern Europe, East Asia, and South Asia, alongside general categories such as sketch and oil painting. All images are generated using the Moonworks Lunara model and intentionally crafted to embody distinct, high-quality aesthetic styles, yielding a first-of-its-kind dataset with substantially higher aesthetic scores, exceeding even aesthetics-focused datasets, and general-purpose datasets by a larger margin. Each image is accompanied by a human-refined prompt and structured annotations that jointly describe salient objects, attributes, relationships, and stylistic cues. Unlike large-scale web-derived datasets that emphasize breadth over precision, the Lunara Aesthetic Dataset prioritizes aesthetic quality, stylistic diversity, and licensing transparency, and is released under the Apache 2.0 license to support research and unrestricted academic and commercial use.

Fields of Study & Subjects

Generative Adversarial Networks and Image Synthesis Advanced Neural Network Applications Digital Media and Visual Art

Publication details

Journal / source
arXiv (Cornell University)
Publication date
12 January 2026
Document type
Preprint