Stable diffusion models reveal a persisting human and AI gap in visual creativity
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917482439114752 |
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| author | Rondini, Silvia Alvarez-Martin, Claudia Angermair-Barkai, Paula Penacchio, Olivier Paz, M. Pelowski, Matthew Dediu, Dan Rodriguez-Fornells, Antoni Cerda-Company, Xim |
| author_facet | Rondini, Silvia Alvarez-Martin, Claudia Angermair-Barkai, Paula Penacchio, Olivier Paz, M. Pelowski, Matthew Dediu, Dan Rodriguez-Fornells, Antoni Cerda-Company, Xim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16814 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stable diffusion models reveal a persisting human and AI gap in visual creativity Rondini, Silvia Alvarez-Martin, Claudia Angermair-Barkai, Paula Penacchio, Olivier Paz, M. Pelowski, Matthew Dediu, Dan Rodriguez-Fornells, Antoni Cerda-Company, Xim Artificial Intelligence Human-Computer Interaction 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. |
| title | Stable diffusion models reveal a persisting human and AI gap in visual creativity |
| topic | Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2511.16814 |