EmoArt: A Multidimensional Dataset for Emotion-Aware Artistic Generation

2025-06-04

AI Summary

Current affective art image generation is hindered by the scarcity of large-scale, fine-grained emotion-annotated datasets. To address this, we introduce EmoArt—the first large-scale, multidimensional artistic emotion dataset—comprising 132,664 artworks spanning 56 artistic styles, with structured annotations across scene descriptions, five visual attributes, valence-arousal dimensions, twelve discrete emotions, and art therapy potential. We conduct systematic affective alignment evaluation on Stable Diffusion 3.5, XL, and FLUX 1, and propose an interpretable, multimodal annotation–integrated assessment protocol. Experimental results demonstrate significant improvements in semantic-emotional consistency for text-to-affective-image generation, establishing new benchmarks for twelve-class emotion recognition and controllable visual attribute synthesis. EmoArt thus provides both high-quality data resources and methodological foundations for affective computing, art therapy applications, and creative design.

Technology Category

• Humans and AI: Emotional Intelligence
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Computer Vision: Diffusion Models for Vision

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Lack of large-scale emotion-annotated art datasets
• Difficulty in generating emotionally expressive artistic images
• Need for benchmarks in emotion-driven image synthesis

Innovation

• EmoArt Dataset with 132,664 emotion-annotated artworks
• Structured annotations for emotion and visual attributes
• Evaluation of text-to-image models for emotional alignment