Emotion-Aware
Artistic Generation
Bridging the gap between visual art and emotion understanding with 132,000+ annotated artworks and multidimensional analysis.
Why EmoArt?
A pioneering large-scale dataset capable of powering the next generation of affective computing and creative AI.
Comprehensive Collection
Spanning Western and Eastern traditions, from Renaissance masterpieces to contemporary abstract movements.
GPT-4o Annotations
Rich, multi-dimensional annotations generated by advanced AI and validated by humans with >85% agreement.
Emotional Depth
Covering 12 representative emotions across the full valence-arousal spectrum for nuanced understanding.
Visual Analysis
Structured decomposition of Brushwork, Composition, Color, Line, and Light.
Benchmark Performance
Evaluating state-of-the-art text-to-image diffusion models.
| Model | Overall Quality | Emotion Alignment | Color | Composition | FID ↓ |
|---|---|---|---|---|---|
| FLUX.1-dev-lora | 0.6604 | 0.6698 | 0.6974 | 0.6698 | 31.65 |
| FLUX.1-dev | 0.6392 | 0.6450 | 0.6503 | 0.6450 | 21.29 |
| PixArt-sigma | 0.6505 | 0.6342 | 0.6746 | 0.6342 | 36.64 |
| Playground | 0.6486 | 0.6247 | 0.6788 | 0.6247 | 42.57 |
| SD3.5 | 0.4350 | 0.6324 | 0.3420 | 0.4324 | 37.96 |
Visual Analysis
- Color Field Painting: FLUX.1-dev-finetuned uses pure blue and white blocks to create a calming atmosphere, capturing the essence of Color Field Painting perfectly.
- Traditional Chinese Painting: The fine-tuned model employs minimalist compositions and soft brushwork, conveying the serenity typical of East Asian aesthetics.
- High-Arousal Emotions: For intense emotions, the model effectively utilizes chaotic line work and asymmetric compositions.
Dataset Composition
A deep dive into the artistic styles and emotional distributions.
Detailed Statistics by Style
| Art Style | Entries | Arousal Distribution | Valence Distribution | Dominant Emotions |
|---|---|---|---|---|
| Abstract Art | 1,759 | High: 51.5% / Low: 48.5% | Pos: 93.5% / Neg: 6.5% | Excited (47%), Calm (39%) |
| Abstract Expressionism | 3,674 | High: 58.7% / Low: 41.3% | Pos: 85.7% / Neg: 14.3% | Excited (47%), Calm (31%) |
| Baroque | 7,995 | High: 21.4% / Low: 78.6% | Pos: 86.2% / Neg: 13.8% | Calm (55%), Contentment (19%) |
| Impressionism | 11,736 | High: 9.9% / Low: 90.1% | Pos: 95.4% / Neg: 4.6% | Calm (67%), Contentment (20%) |
| Realism | 15,307 | High: 8.0% / Low: 92.0% | Pos: 88.9% / Neg: 11.1% | Calm (65%), Contentment (20%) |
| Expressionism | 10,065 | High: 36.2% / Low: 63.8% | Pos: 75.9% / Neg: 24.1% | Calm (42%), Excited (21%), Sad (11%) |
| Chinese Painting | 4,157 | High: 0.2% / Low: 99.8% | Pos: 99.9% / Neg: 0.1% | Calm (89%), Contentment (10%) |
| Ukiyo-e | 1,730 | High: 13.9% / Low: 86.1% | Pos: 95.2% / Neg: 4.8% | Calm (74%), Contentment (11%) |
| Full statistical breakdown available in the dataset documentation. | ||||
Citation
If you use EmoArt in your research, please cite our paper.