EmoArt: A Multidimensional Dataset for Emotion-Aware Artistic Generation

TL;DR

EmoArt introduces a large, richly annotated dataset of artworks with emotional and stylistic labels to improve emotion-aware artistic image generation using diffusion models.

Contribution

The paper presents EmoArt, a comprehensive emotion-annotated art dataset with diverse styles and detailed annotations, enabling systematic evaluation of emotion-driven image synthesis models.

Findings

Diffusion models vary in their ability to generate emotion-aligned images.

EmoArt provides benchmarks for evaluating emotion-aware artistic generation.

The dataset supports research in affective computing and computational art.

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.