EmoScene: A Dual-space Dataset for Controllable Affective Image Generation
Text-to-image diffusion models achieve high visual fidelity, yet fine-grained affective control remains difficult because textual emotion cues often fail to specify the visual perceptual factors underlying affective expression. Existing visual-affect datasets are likewise often limited to discrete labels, specific domains, or limited supervision of perceptual attributes. We introduce EmoScene, a large-scale dual-space dataset for controllable affective image generation, containing 1.2M images across more than 300 scene categories. Its affective space jointly represents discrete emotions and continuous valence--arousal--dominance (VAD), its perceptual space records measurable appearance at...
| Authors | Li He, Longtai Zhang, Wenqiang Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 1 April 2026 |
| Last updated at source | 3 August 2026 |
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