Imaginique Expressions: Tailoring Personalized Short-Text-to-Image Generation Through Aesthetic Assessment and Human Insights

Abstract

The text-to-image task, a critical branch of computer vision and image processing, has witnessed remarkable advancements fueled by the abundance of realistic data and rapid AI innovation. However, existing research often overlooks scenarios involving sparse textual input and fails to incorporate human personalized preferences into the generative process. To address these gaps, we propose a novel AI methodology: personalized short-text-to-image generation through aesthetic assessment and human insights. Our approach introduces a symmetry between personalized aesthetic preferences and the generated images by leveraging a data-driven personality encoder (PE) to extract personal information and embed it into a Big Five personality trait-based image aesthetic assessment (BFIAA) model. This model harmonizes aesthetic preferences with the generative process by adapting the stable diffusion framework to align with personalized assessments. Experimental results demonstrate the effectiveness of our method: the PE module achieves an accuracy of 98.1%, while the BFIAA model surpasses the baseline by 13% on the PLCC metric, accurately reflecting human aesthetic preferences. Furthermore, our adapted generation model improves convergence loss by over 10% compared to the base model, consistently producing personalized images that are more aligned with human preferences.