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Understanding and Creating Art with AI: Review and Outlook (2102.09109v1)

Published 18 Feb 2021 in cs.CV, cs.AI, and cs.MM

Abstract: Technologies related to AI have a strong impact on the changes of research and creative practices in visual arts. The growing number of research initiatives and creative applications that emerge in the intersection of AI and art, motivates us to examine and discuss the creative and explorative potentials of AI technologies in the context of art. This paper provides an integrated review of two facets of AI and art: 1) AI is used for art analysis and employed on digitized artwork collections; 2) AI is used for creative purposes and generating novel artworks. In the context of AI-related research for art understanding, we present a comprehensive overview of artwork datasets and recent works that address a variety of tasks such as classification, object detection, similarity retrieval, multimodal representations, computational aesthetics, etc. In relation to the role of AI in creating art, we address various practical and theoretical aspects of AI Art and consolidate related works that deal with those topics in detail. Finally, we provide a concise outlook on the future progression and potential impact of AI technologies on our understanding and creation of art.

Understanding and Creating Art with AI: Review and Outlook

The intersection of AI and art represents a burgeoning area of inquiry with profound implications for both technological advancement and artistic creativity. The paper "Understanding and Creating Art with AI: Review and Outlook" offers a meticulous examination of this interdisciplinary field by dissecting two primary dimensions: the use of AI in the analysis of existing artworks and the generation of novel artistic creations.

AI in Art Analysis

The utilization of AI technologies for art analysis has gained traction, largely due to the widespread digitization of artwork collections. Various computational methods have been developed to classify, categorize, and retrieve art data, with tasks including classification, object detection, and multimodal representations utilizing deep learning architectures, primarily Convolutional Neural Networks (CNNs). These methodologies have revolutionized the remote examination of digitized art, often focusing on aspects such as style and genre recognition, or identifying iconographic elements and aesthetic attributes.

The availability of robust datasets, such as those listed in the paper, facilitates the training of deep learning models capable of performing intricate analyses of art collections. Such datasets enable tasks ranging from basic classification of artists or styles to complex multimodal retrieval challenges that map visual and textual data into unified semantic spaces. The advent of transfer learning has further enhanced the capacity of AI to discern subtle artistic traits, leveraging feature representations learned from large datasets like ImageNet.

AI in Creating Art

AI's role in the creation of art primarily stems from the introduction of techniques such as Neural Style Transfer (NST) and Generative Adversarial Networks (GANs). NST allows for artistic stylization of images by replicating artist-specific styles onto new content, though it often falls short of being considered original art. GANs, on the other hand, present a transformative approach, enabling the generation of novel and creative visual content. Advanced iterations, such as AICAN, attempt to foster creativity by deviating significantly from typical style norms while maintaining artistic coherence.

Alongside these, breakthroughs in multimodal AI models, exemplified by OpenAI's DALL·E, suggest a future where AI systems can construct visual art from linguistic input, evolving the dialogue between textual inspiration and visual creation.

Conceptual Implications and Challenges

The infiltration of AI into the art domain necessitates a critical discourse on issues such as authorship, originality, and the ethical ramifications of AI-generated art. The authorship question becomes complex when AI systems generate works, as demonstrated by cases such as Christie’s auctioning of AI-generated artwork, which sparked debates about credit and ownership.

Furthermore, while current AI technologies demonstrate remarkable capability in emulating artistic styles and generating novel patterns, questions about the genuine creativity and autonomy of these systems persist. Coherent with the historical context, AI art sits on a continuum of generative art practices that predate the contemporary technological landscape, tracing back to early computational and rule-based art.

Future Prospects

The continued evolution of AI technologies promises to reshape both qualitative and quantitative approaches to art. Increased adoption of digital methods in art historical research is anticipated, bolstered by AI’s ability to mine and interpret vast datasets. In the field of creative production, future AI models may leverage sophisticated multimodal frameworks for creating art that transcends single-medium limitations.

Moreover, the impact of AI on the art market, driven by burgeoning concepts like CryptoArt and blockchain-based transactions, signals a paradigm shift that extends beyond artistic creation to encompass how art is traded and valued. Thus, as AI matures in its capability to navigate the artistic terrain, it will undoubtedly influence the craftsmanship, appreciation, and distribution of art in unprecedented ways.

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Authors (2)
  1. Eva Cetinic (9 papers)
  2. James She (6 papers)
Citations (249)
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