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New EmoScene dataset enhances controllable affective image generation

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced EmoScene, a large-scale dataset designed to improve controllable affective image generation. The dataset contains 1.2 million images across over 300 scene categories, featuring a dual-space representation that includes discrete emotions and continuous VAD (valence-arousal-dominance) dimensions, alongside measurable appearance attributes. To demonstrate its utility, the team developed AffectCtrl, a system that enhances frozen diffusion models for categorical emotion generation and continuous control over VAD, brightness, and saturation, achieving high accuracy and strong correlations across control axes. AI

IMPACT Enables more nuanced control over emotional expression in AI-generated images, potentially impacting creative tools and media.

RANK_REASON The cluster describes a new dataset and associated model for image generation research, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New EmoScene dataset enhances controllable affective image generation

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The cluster describes a new dataset and associated model for image generation research, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Li He, Longtai Zhang, Wenqiang Zhang, Yan Wang, Lizhe Qi ·

    EmoScene: A Dual-space Dataset for Controllable Affective Image Generation

    arXiv:2604.00933v2 Announce Type: replace Abstract: 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. …

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