ReART: Reference-Guided Retrieval and Refinement for Emotion-Aware Art Generation
Emotion-aware artistic image generation requires a model to satisfy semantic content, artistic style, and target emotion simultaneously. The key challenge is that artistic captions conflate these axes into underspecified free-form text, making fine-grained visual attributes such as brushwork, composition, and tonal atmosphere difficult to ground concretely. We present ReART, a reference-guided retrieval and refinement framework. Our method decomposes test captions and each image annotation in the EmoArt database into structured visual fields, and performs field-wise retrieval over subject, layout, brush-line, and tone-mood dimensions to retrieve role-specific visual references that supply...
| Authors | Qianqian Tang, Jiayi Gao, Ting Lei et al. |
|---|---|
| Primary category | cs.MM |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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