arXiv:2606.13247v1 Announce Type: new Abstract: Text-to-image diffusion models have achieved impressive results in synthesizing high-quality images from natural language prompts. However, commonly used prompting strategies remain relatively generic, limiting the model's ability to accurately express emotional intent and nuanced affective attributes. This work proposes EPIG, a method that enhances emotional expressiveness at the prompt level prior to image generation. Grounded in psychologically informed emotion representations (valence-arousal) and leveraging structured, role-aware prompt enri
The rapid advancement of text-to-image models has exposed the limitations of generic prompting, making emotional expressiveness a natural next frontier for improvement.
Enhanced emotional capacity in image generation signifies a step towards more nuanced, personalized, and human-like AI creativity, potentially impacting a wide array of digital content industries.
Prompting for image generation can now move beyond functional descriptions to incorporate psychological and affective intent, allowing for more precise and emotionally resonant visual outputs.
- · Digital artists and designers
- · Advertising and marketing agencies
- · Creative AI platforms
- · Entertainment industry
- · Generic image generation services
- · Content creators relying solely on basic prompts
Image generation models will produce content with richer emotional depth, improving user satisfaction and creative possibilities.
The ability to evoke specific emotions via AI-generated imagery could lead to more persuasive and emotionally manipulative content.
As AI models better understand human emotion, their integration into interfaces could foster more empathetic and responsive human-computer interactions.
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Read at arXiv cs.AI