Stable diffusion models reveal a persisting human and AI gap in visual creativity

TL;DR

This study compares human and AI visual creativity, revealing a persistent gap where AI models lag behind humans, especially in perceptual nuance and contextual sensitivity, despite increased human guidance improving AI output.

Contribution

It highlights the differences in visual creativity between humans and AI, emphasizing the unique perceptual and contextual challenges AI faces in visual domains.

Findings

Visual Artists are most creative, followed by Non Artists, then Human Inspired AI, and Self Guided AI.

More human guidance improves AI creativity, making it comparable to Non Artists.

Humans and AI differ significantly in how they judge creativity.

Abstract

While recent research suggests Large Language Models match human creative performance in divergent thinking tasks, visual creativity remains underexplored. This study compared image generation in human participants (Visual Artists and Non Artists) and using an image generation AI model (two prompting conditions with varying human input: high for Human Inspired, low for Self Guided). Human raters (N=255) and GPT4o evaluated the creativity of the resulting images. We found a clear creativity gradient, with Visual Artists being the most creative, followed by Non Artists, then Human Inspired generative AI, and finally Self Guided generative AI. Increased human guidance strongly improved GenAI's creative output, bringing its productions close to those of Non Artists. Notably, human and AI raters also showed vastly different creativity judgment patterns. These results suggest that, in contrast to language centered tasks, GenAI models may face unique challenges in visual domains, where creativity depends on perceptual nuance and contextual sensitivity, distinctly human capacities that may not be readily transferable from language models.