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

• URL: http://arxiv.org/abs/2304.07999v2
• Date: Thu, 22 Feb 2024 14:27:15 GMT
• Title: Everyone Can Be Picasso? A Computational Framework into the Myth of Human versus AI Painting
• Authors: Yilin Ye, Rong Huang, Kang Zhang, Wei Zeng
• Abstract summary: We develop a computational framework combining neural latent space and aesthetics features with visual analytics to investigate the difference between human and AI paintings. We find that AI artworks show distributional difference from human artworks in both latent space and some aesthetic features like strokes and sharpness. 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.
• Score: 8.031314357134795
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Organic or Diffused: Can We Distinguish Human Art from AI-generated Images? [24.417027069545117]
  Distinguishing AI generated images from human art is a challenging problem. A failure to address this problem allows bad actors to defraud individuals paying a premium for human art and companies whose stated policies forbid AI imagery. We curate real human art across 7 styles, generate matching images from 5 generative models, and apply 8 detectors.
  arXiv  Detail & Related papers  (2024-02-05T17:25:04Z)
• AI Art Neural Constellation: Revealing the Collective and Contrastive State of AI-Generated and Human Art [36.21731898719347]
  We conduct a comprehensive analysis to position AI-generated art within the context of human art heritage. Our comparative analysis is based on an extensive dataset, dubbed ArtConstellation'' Key finding is that AI-generated artworks are visually related to the principle concepts for modern period art made in 1800-2000.
  arXiv  Detail & Related papers  (2024-02-04T11:49:51Z)
• AIxArtist: A First-Person Tale of Interacting with Artificial Intelligence to Escape Creative Block [20.96181205379132]
  The future of the arts and artificial intelligence (AI) is promising as technology advances. This workshop pictorial puts forward first-person research that shares interactions between an HCI researcher and AI. The paper explores two questions: How can AI support artists' creativity, and what does it mean to be explainable in this context.
  arXiv  Detail & Related papers  (2023-08-22T13:15:29Z)
• Artificial Intelligence and Aesthetic Judgment [29.71278672770529]
  Generative AIs produce creative outputs in the style of human expression. We argue that encounters with the outputs of modern generative AI models are mediated by the same kinds of aesthetic judgments.
  arXiv  Detail & Related papers  (2023-08-21T17:40:54Z)
• Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images [66.20578637253831]
  There is a growing concern that the advancement of artificial intelligence (AI) technology may produce fake photos. This study aims to comprehensively evaluate agents for distinguishing state-of-the-art AI-generated visual content.
  arXiv  Detail & Related papers  (2023-04-25T17:51:59Z)
• Pathway to Future Symbiotic Creativity [76.20798455931603]
  We propose a classification of the creative system with a hierarchy of 5 classes, showing the pathway of creativity evolving from a mimic-human artist to a Machine artist in its own right. In art creation, it is necessary for machines to understand humans' mental states, including desires, appreciation, and emotions, humans also need to understand machines' creative capabilities and limitations. We propose a novel framework for building future Machine artists, which comes with the philosophy that a human-compatible AI system should be based on the "human-in-the-loop" principle.
  arXiv  Detail & Related papers  (2022-08-18T15:12:02Z)
• On some Foundational Aspects of Human-Centered Artificial Intelligence [52.03866242565846]
  There is no clear definition of what is meant by Human Centered Artificial Intelligence. This paper introduces the term HCAI agent to refer to any physical or software computational agent equipped with AI components. We see the notion of HCAI agent, together with its components and functions, as a way to bridge the technical and non-technical discussions on human-centered AI.
  arXiv  Detail & Related papers  (2021-12-29T09:58:59Z)
• A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interaction [70.11080854486953]
  We propose a user-centred framework for XAI that focuses on its social-interactive aspect. The framework aims to provide a structure for interactive XAI solutions thought for non-expert users.
  arXiv  Detail & Related papers  (2021-09-27T09:56:23Z)
• Trustworthy AI: A Computational Perspective [54.80482955088197]
  We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
  arXiv  Detail & Related papers  (2021-07-12T14:21:46Z)
• Comparing Visual Reasoning in Humans and AI [66.89451296340809]
  We created a dataset of complex scenes that contained human behaviors and social interactions. We used a quantitative metric of similarity between scene descriptions of the AI/human and ground truth of five other human descriptions of each scene. Results show that the machine/human agreement scene descriptions are much lower than human/human agreement for our complex scenes.
  arXiv  Detail & Related papers  (2021-04-29T04:44:13Z)
• Cognitive Anthropomorphism of AI: How Humans and Computers Classify Images [0.0]
  Humans engage in cognitive anthropomorphism: expecting AI to have the same nature as human intelligence. This mismatch presents an obstacle to appropriate human-AI interaction. I offer three strategies for system design that can address the mismatch between human and AI classification.
  arXiv  Detail & Related papers  (2020-02-07T21:49:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.