Everyone Can Be Picasso? A Computational Framework into the Myth of Human versus AI Painting

Yilin Ye; Rong Huang; Kang Zhang; Wei Zeng

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arXiv · 2304.07999

Everyone Can Be Picasso? A Computational Framework into the Myth of Human versus AI Painting

Yilin Ye·Rong Huang·Kang Zhang·Wei Zeng

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Abstract

The recent advances of AI technology, particularly in AI-Generated Content (AIGC), have enabled everyone to easily generate beautiful paintings with simple text description. With the stunning quality of AI paintings, it is widely questioned whether there still exists difference between human and AI paintings and whether human artists will be replaced by AI. To answer these questions, we develop a computational framework combining neural latent space and aesthetics features with visual analytics to investigate the difference between human and AI paintings. First, with categorical comparison of human and AI painting collections, we find that AI artworks show distributional difference from human artworks in both latent space and some aesthetic features like strokes and sharpness, while in other aesthetic features like color and composition there is less difference. Second, with individual artist analysis of Picasso, we show human artists' strength in evolving new styles compared to AI. Our findings provide concrete evidence for the existing discrepancies between human and AI paintings and further suggest improvements of AI art with more consideration of aesthetics and human artists' involvement.

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BibTeX RIS

Yilin Ye, Rong Huang, Kang Zhang, Wei Zeng. 2023-04-17. Everyone Can Be Picasso? A Computational Framework into the Myth of Human versus AI Painting. https://arxiv.org/abs/2304.07999

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