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

arXiv: https://arxiv.org/abs/2504.20340 · HTML: https://mlsanity.com/paper/2504.20340/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 32 references that are in the map.
- First posted on arXiv 29 Apr 2025.

## Builds on (most cited first, 18 of 18)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [Iterative Prompt Learning for Unsupervised Backlit Image Enhancement](https://mlsanity.com/paper/2303.17569/) (2023)
- [Improving Text-to-Image Consistency via Automatic Prompt Optimization](https://mlsanity.com/paper/2403.17804/) (2024)
- [Read, Revise, Repeat: A System Demonstration for Human-in-the-loop Iterative Text Revision](https://mlsanity.com/paper/2204.03685/) (2022)
- [Shift-tolerant Perceptual Similarity Metric](https://mlsanity.com/paper/2207.13686/) (2022)
- [Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering](https://mlsanity.com/paper/2303.13534/) (2023)
- [GenAssist: Making Image Generation Accessible](https://mlsanity.com/paper/2307.07589/) (2023)
- [Human Image Generation: A Comprehensive Survey](https://mlsanity.com/paper/2212.08896/) (2022)
- [Exploring the Impact of AI-generated Image Tools on Professional and Non-professional Users in the Art and Design Fields](https://mlsanity.com/paper/2406.10640/) (2024)
- [A Word is Worth a Thousand Pictures: Prompts as AI Design Material](https://mlsanity.com/paper/2303.12647/) (2023)
- [Prompt Refinement or Fine-tuning? Best Practices for using LLMs in Computational Social Science Tasks](https://mlsanity.com/paper/2408.01346/) (2024)
- [Capability-aware Prompt Reformulation Learning for Text-to-Image Generation](https://mlsanity.com/paper/2403.19716/) (2024)
- [Promptly Yours? A Human Subject Study on Prompt Inference in AI-Generated Art](https://mlsanity.com/paper/2410.08406/) (2024)

## Built on by (most cited first, 2 of 2)

- [Prompt and Circumstances: Evaluating the Efficacy of Human Prompt Inference in AI-Generated Art](https://mlsanity.com/paper/2601.17379/) (2026)
- [CLPIPS: A Personalized Metric for AI-Generated Image Similarity](https://mlsanity.com/paper/2604.01234/) (2026)

## Abstract (arXiv)

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 AI-generated images often involves refining the input prompt iteratively to achieve desired visual outcomes. This study focuses on the relatively underexplored 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.