A Picture is Worth a Thousand Prompts? Efficacy of Iterative Human-Driven Prompt Refinement in Image Regeneration Tasks

A Picture is Worth a Thousand Prompts? Efficacy of Iterative Human-Driven Prompt Refinement in Image Regeneration T asks Khoi Trinh 1, Scott Seidenberger 1, Raveen Wijewickrama 2, Murtuza Jadliwala 2, Anindya Maiti 1 1 University of Oklahoma 2 University of Texas at San Antonio khoitrinh@ou.edu, Abstract With AI-generated content becoming ubiquitous across the web, social media, and other digital platforms, it is vital to examine how such content are inspired and generated. The creation of AIgenerated images often involves refining the input prompt iteratively to achieve desired visual outcomes. This study focuses on the relatively un-derexplored concept of image regeneration using AI, in which a human operator attempts to closely recreate a specific target image by iteratively refining their prompt. Image regeneration is distinct from normal image generation, which lacks any predefined visual reference. A separate challenge lies in determining whether existing image similarity metrics (ISMs) can provide reliable, objective feedback in iterative workflows, given that we do not fully understand if subjective human judgments of similarity align with these metrics. Consequently, we must first validate their alignment with human perception before assessing their potential as a feedback mechanism in the iterative prompt refinement process. To address these research gaps, we present a structured user study evaluating how iterative prompt refinement affects the similarity of regenerated images relative to their targets, while also examining whether ISMs capture the same improvements perceived by human observers. Our findings suggest that incremental prompt adjustments substantially improve alignment, verified through both subjective evaluations and quantitative measures--underscoring the broader potential of iterative workflows to enhance generative AI content creation across various application domains. 1 Introduction The rise of AI-generated content on online platforms has made it crucial to investigate how this type of content is created, specifically through the iterative processes of image generation and regeneration. While prior work has explored AI-led iterative refinement, this paper highlights the human's leading role in refining prompts and improving outcomes through their own judgment and control. The field of generative artificial intelligence (GenAI) has recently seen significant advancements, particularly in the development of text-to-image ( txt2img) models. These models provide an easy and fast process for creating high-quality artwork.