EmoArt: A Multidimensional Dataset for Emotion-Aware Artistic Generation
Journal:
arXiv
Published Date:
Jun 4, 2025
Abstract
With the rapid advancement of diffusion models, text-to-image generation has
achieved significant progress in image resolution, detail fidelity, and
semantic alignment, particularly with models like Stable Diffusion 3.5, Stable
Diffusion XL, and FLUX 1. However, generating emotionally expressive and
abstract artistic images remains a major challenge, largely due to the lack of
large-scale, fine-grained emotional datasets. To address this gap, we present
the EmoArt Dataset -- one of the most comprehensive emotion-annotated art
datasets to date. It contains 132,664 artworks across 56 painting styles (e.g.,
Impressionism, Expressionism, Abstract Art), offering rich stylistic and
cultural diversity. Each image includes structured annotations: objective scene
descriptions, five key visual attributes (brushwork, composition, color, line,
light), binary arousal-valence labels, twelve emotion categories, and potential
art therapy effects. Using EmoArt, we systematically evaluate popular
text-to-image diffusion models for their ability to generate emotionally
aligned images from text. Our work provides essential data and benchmarks for
emotion-driven image synthesis and aims to advance fields such as affective
computing, multimodal learning, and computational art, enabling applications in
art therapy and creative design. The dataset and more details can be accessed
via our project website.
