Human Artists Research Articles

Continual few-shot patch-based learning for anime-style colorization

The automatic colorization of anime line drawings is a challenging problem in production pipelines. Recent advances in deep neural networks have addressed this problem; however, collectingmany images of colorization targets in novel anime work before the colorization process starts leads to chicken-and-egg problems and has become an obstacle to using them in production pipelines. To overcome this obstacle, we propose a new patch-based learning method for few-shot anime-style colorization. The learning method adopts an efficient patch sampling technique with position embedding according to the characteristics of anime line drawings. We also present a continuous learning strategy that continuously updates our colorization model using new samples colorized by human artists. The advantage of our method is that it can learn our colorization model from scratch or pre-trained weights using only a few pre- and post-colorized line drawings that are created by artists in their usual colorization work. Therefore, our method can be easily incorporated within existing production pipelines. We quantitatively demonstrate that our colorizationmethod outperforms state-of-the-art methods.

Emulating Artistic Expressions in Robot Painting: A Stroke-Based Approach

Representing art using a robotic system is part of artificial intelligence in our lives, especially in the realm of emotional expression. Developing a painting robot involves addressing how to enable the robot to emulate human artistic processes, which often include imprecise techniques or errors akin to those made by human artists. This paper discusses our development of an innovative painting robot utilizing the sim-to-real approach within learning technology. Specifically, this pipeline operates under a deep reinforcement learning (DRL) framework designed to learn drawing strategies from training data derived from real-world settings, aiming for the robot’s proficiency in emulating human artistic expressions. Accordingly, the framework comprises two modules when given a target drawing image: the first module trains in a simulated environment to break down the target image into individual strokes; the second module then learns how to execute these strokes in a real environment. Our experiments have shown that this system can meet our objectives effectively.

Participation in Artificial Intelligence: Toward a Tillichian Reading of AI-Produced Images

This paper argues that Paul Tillich's theology of art is an effective approach to assessing images generated by artificial intelligence (AI). Tillich's theology of art and concept of participation demonstrate its limits and provide a helpful supplement to the dominant approach of focusing on AI creativity and consciousness, particularly through the framework of philosopher Margaret Boden. In Tillich's theology of art, there is an existential experience of being grasped into participation in the ground of being through the artwork that comes through participation in the art. In participating in the art, one also participates in that artist's contextual answer to the question of ultimate meaning. This article finds that AI-generated images, on their own, lack intentionality and desire to express participation in the spiritual presence and so do not provide this "religious style." Rather, the participation of a human artist crafting text prompts and curating the produced images is necessary along with the AI software.

“Making the unimaginable imaginable?” The power of artmaking in understanding animal vulnerabilities and “humanimal” relationality in Organization Studies

The vulnerabilities of nonhuman animals in human–animal relationships have received scant attention in Organization Studies (OS). What could OS scholars learn about animal vulnerability and “humanimal” relationality by turning to the context of artmaking, where sensate animals, human artists, spaces, materialities, artworks, affects and critical audiences come together? Building on feminist vulnerability literature and insight from posthumanist affirmative ethics, we here analyze Finnish artists Terike Haapoja and Laura Gustafsson’s artmaking, works, and their exhibition Siat – Pigs in particular, where the agency and vulnerability of animals can be conceived in non-anthropocentric, immersive and affective ways. We contribute to OS research by demonstrating the affective power of posthumanist artmaking that comes with the radical repositioning of the human in relation to others, as well as political motivation to elicit empathy for the plight of animals in the factory-farming complex. Specifically, we show how these insights can illuminate what is currently not centered or discussed enough in OS, help us to better acknowledge co-constituted humanimal vulnerabilities, and extend discussions on empathy in OS to include (hyper-vulnerable) animals in the factory-farming complex. We contend that by extending vulnerability ascriptions to animals, caring with the unseen and silenced agents in society, acknowledging our shared vulnerability and by taking further action, we can gradually change the exploitive ways in which humans have treated other animals in our organized society, and more emphatically work for the well-being of the many unseen “others.”

AIGC (Artificial Intelligence Generated Content) infringes the copyright of human artists

With the rapid development of artificial intelligence technology, content generated by artificial intelligence has been rapidly applied to people's lives. At the same time, it is also accompanied by many infringement lawsuits, whether AIGC has really caused different degrees of infringement to human artists. Through the analysis of the existing literature on copyright issues and the walkthrough of Stable Diffusion, an AI-generated image platform, this article digs into the main factors that the AI-generated platform causes infringements on human artists. Provide references for using AI by enterprises and related media, and let more scholars pay attention to this issue. The study found that in the workflow of the AI generation platform, taking Stable Diffusion as an example, the two processes of model training and image generation may cause copyright infringement to a certain extent. Based on this, the AI generation platform has unauthorized use of copyright works, excessive plagiarism and adaptation of copyright works, and the generated images are not marked with watermarks or sources, which damages the copyright owner's rights.

Artists or art thieves? media use, media messages, and public opinion about artificial intelligence image generators

AbstractThis study investigates how patterns of media use and exposure to media messages are related to attitudes about artificial intelligence (AI) image generators. In doing so, it builds on theoretical accounts of media framing and public opinion about science and technology topics, including AI. The analyses draw on data from a survey of the US public (N = 1,035) that included an experimental manipulation of exposure to tweets framing AI image generators in terms of real art, artists’ concerns, artists’ outrage, or competing interpretations. The results show that technology news use and science fiction viewing predicted support for AI art but also predicted belief that AI image generators will take jobs and steal art styles from human artists. In addition, the experimental results demonstrate that exposure to specific media messages can influence these responses. The findings carry implications for understanding the future adoption, use, and regulation of AI image generators.

Comprehending AI's Role in Literature and Arts from a Transhumanist Perspective

This research paper embarks on a journey into the transhumanistic intersection between human intelligence and Artificial Intelligence (AI) in the context of producing literature and arts. It argues that human-AI collaboration is a catalytic process for expansion and innovation, with AI emerging as a potential creative partner. This exploration delves into how AI can serve as an extension of human articulation, enhancing artistic capabilities and broadening the horizons of artistic expression. The study underscores the pivotal role of AI in artistic domains by tracing its historical development in creative fields, examining collaborations between human artists and AI systems, and exploring real-world applications to illuminate the potential advantages and challenges of AI-aided creativity. The paper further delves into the ethical and philosophical considerations surrounding authorship, authenticity, and human intervention in AI-generated elements, as well as their societal and cultural implications, offering a glimpse into how AI applications are reshaping contemporary society. Moreover, the research probes into how AI-generated art disrupts traditional aesthetic art forms, reshapes artistic norms, and fosters versatile interpretations. By scrutinizing the potential impacts and risks associated with AI in literature and arts, this research paper aims to shed light on the symbiotic power of AI as an extension of human intelligence in the creative spectrum within the framework of Transhumanism.

Innovative Application of Chinese Landscape Culture Painting Elements in AI Art Generation

Abstract In recent years, the swift advancement of artificial intelligence (AI) has significantly propelled the capabilities of data generation, a pivotal technology underpinning AI development. This has led to remarkable innovations across various domains, including images, text, and speech. In this study, we introduce a sophisticated image translation network tailored for AI-driven art generation of landscape painting elements. This network is enhanced by integrating a self-attention mechanism with orthogonal Jacobi regularization, aiming to elevate the quality of the generated artwork. Furthermore, we developed a landscape style transfer model using the image above translation network to encapsulate elements of Chinese landscape cultural paintings. This model emulates the artistic process employed by human artists, effectively capturing the essence of the content and infusing the intended style. The efficacy of the generated images is quantitatively assessed through Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) values. Comparative analysis of the style transfer outcomes across three models reveals that the Generative Adversarial Network (GAN) model exhibits the lowest PSNR and SSIM values. Although the CycleGAN model achieves commendable style transfer, its visual impact remains less pronounced in comparison to the Wasserstein GAN (WGAN). Subjective evaluations of image quality and style fidelity between the generated images and the input stylized images indicate that the WGAN outperforms other models. It achieves an average content evaluation score of 4.11 and a style score of 4.2 across nine style transformations, surpassing the average scores obtained by competing models.

Will AI Art Make Human Artists Obsolete

Will AI Art Make Human Artists Obsolete

Multistage guidance on the diffusion model inspired by human artists’ creative thinking

Multistage guidance on the diffusion model inspired by human artists’ creative thinking

Artificial intelligence and digital art: current state and development prospects

The article analyzes the use of artificial intelligence in various types of digital art at the current level of their development and outlines the possibilities of external ways of such interaction. The author considers specific artistic examples, which see that the use of artificial intelligence in art develops a complex art history and philo-sophical questions: what is art? how does computer technology change aesthetics? where is the line between technology and creativity? who is considered the author of the work generated by neuronets? is it possible to compare anthropocentric and non-anthropocentric forms of creativity? etc. The researcher demonstrates different approaches to the chosen topic, citing antagonistic points of view of scientists. Some of them believe that art with the help of a computer is the next step in the development of avant-garde trends, while others see these trends as the degradation and even the death of art. In digital painting, we are already faced with the use of works (the most famous example is the android-artist Ai-Da) who know how to draw in different techniques and with the use of different tools. The victory of a painting generated by a neural network at an art competition raised questions about its au-thorship and the admissibility of participation of such works in competitions for art-ists. Discussions on the ArtStation platform indicate that many artists do not per-ceive works made with the help of artificial intelligence as "real" works of art. In mu-sic today, it is possible not just to generate melodies, but to create an imitation of certain styles or composers, which raises questions about the nature of true creativi-ty and its limits. Another direction of using artificial intelligence in music is the crea-tion of virtual singers who become mega-popular. Such an example is given by the Japanese virtual singer Hatsune Miku, who is a hologram with an android interface and an artificially generated voice. In the field of video games, artificial intelligence knows how to create not only levels, but already generate game target worlds and complete games. Artificial intelligence in photography can generate images of non-existent people. At the same time, it is unlikely that artificial intelligence can com-pletely replace a human artist, after which he learns from the previously created ma-terial. This makes it possible to say that he left real creativity and knows how to compile in advance, and not to generate original artistic solutions.

Potensi Artificial Intelligence dalam Dunia Kreativitas Desain

The shift in cultural practices towards digital usage has led companies and product brands to become increasingly adept at utilizing social media for promotion and branding. Creating social media content requires creativity, interactivity, attractiveness, and speed, driving a growing demand for design skills. To support the design process, designers rely on various tools, including AI-integrated applications, such as Canva, Figma, Picsart, Adobe Photoshop, Adobe Lightroom, and others. These tools enhance graphic designers' productivity and enable them to edit images, create artworks, and produce creative content for social media. 
 The research explores the usage of AI technology in the field of design and art. It examines several AI applications, including image segmentation, image enhancement, image-to-image translation, face attribute manipulation, creating new artistic expressions, detecting fake paintings, 3D art, and AI influencers. AI has positively impacted the creative design world by enabling artists to explore new artistic expressions, generate more personal and interactive artworks, and streamline the design workflow. AI tools have also facilitated inclusive design, making creative content accessible to diverse audiences. However, there are concerns that AI may replace human artists and create art lacking soul or authenticity. The research aims to provide an overview of the existing studies on AI in design to help designers understand the potential of AI in the creative domain. It also discusses the positive and negative implications of AI's integration into the world of design, emphasizing the need for ethical considerations and the preservation of human artistic contributions.

Transformations of the Creator Man’s image in art

Art is not only a form of self-expression, but it is self-knowledge and self-acceptance. From this position, the Creator Man’s image in art represents research optics in which the transformations occurring in human existence become visible. These transformations are connected by a person’s experience of the meaning and values of his own existence in their relationship with the world. Understanding art as an existential experience, and therefore understanding the image of the Creator Man as artistic existence, opens up the possibility of understanding the «human code» contained in artistic creativity. The Creator Man’s image is one of the main projects of art, which has gone through many stages during its implementation. The article consistently examines different historical periods of artistic creativity. Their analysis shows the gradual growth of the individual principle of the artist, which had its own version of the «counterweight» in different eras. On this basis, the author of the study puts forward a hypothesis: when a human artist first realized his exceptional creative capabilities in understanding and transforming reality, his individualized position of a creator (a thesis) always had its antithesis. At the same time, the thesis, which is a constant first established by Renaissance culture, could be modified depending on the dominant social, cultural and artistic meanings and values in a given era, but it never disappeared from art. While different antitheses dominated in different historical periods. The author examines various manifestations of this contradiction using the examples of baroque art, classicism, romanticism, realism and modernism. This allows us to fix the reference points in the development of the Creator Man’s image. Analysis of historical transformations of the Creator Man’s individualization in art makes it possible to trace the changes that took place in artistic consciousness, reflecting the image of the world with the anthropos operating in it.

A manifesto for Intelligent Experiences

Let's look beyond the impressive capabilities of current AI tools, to envision 'intelligent experiences' that can surprise and delight us, are culturally enriching, and are inclusive, fair and environmentally sound. Intelligent experiences are encounters with creative works infused with new and emerging technologies. They encompass sensory, emotional and intellectual engagement with various interfaces and artifacts powered by machine learning models. These experiences are where humans meaningfully engage with machine learning's predictive capabilities. They manifest the outputs of machine logic in forms that humans can understand and interact with. Crucially, intelligent experiences foster serendipitous, unexpected interactions rather than trying to replicate interactions learned from the past. This goes beyond efficiency, or mimicking the works of human artists. Intelligent experiences are celebrations of diverse interpretations and interactions, and mediators of constructive discussions rather than heated exchange. They provoke us to search for meaning and come to new interpretations of cultural works and ultimately of ourselves. The arts, like any other field, have a responsibility to use technologies responsibly, so we call for intelligent experiences to be not only delightful and enriching, but also socially and environmentally just. They need to express a multiplicity of data rights and ownership, and acknowledge and reward effortful human creative practice. For this to work, we need to ask questions of AI itself, and consider how the technology needs to evolve to make a lasting impact on human creativity. This vision for intelligent experiences will require AI to become more subjective, playful, able to deal with ambiguity, open to failure, improvised, negotiated, and being open to critique from those who engage with it. We believe intelligent experiences can help to radically change how we think about AI design, moving beyond the current paradigm of learning patterns from large amounts of data, to embrace human traits such as bias, disagreement, and uncertainty as a signal with creative potential rather than noise that needs to be removed.

What artists want from AI Tools

At The New Real, we want to reflect and represent those at the forefront of art and AI – and to work with them to develop actionable strategies and signposts for practitioners. Here Eoghan O’Keeffe, an artist and toolmaker, shares with us the key features and capabilities artists are looking for in AI tools as these technologies move forward. We wanted to know: Looking beyond the text prompt, what do artists want to see from a new generation of tools? How can we give human artists greater agency in co-creation with AI? How can artists create works based on rich understanding of models? And how important is it that tools are legible, interpretable, or configurable?

Bias against AI art can enhance perceptions of human creativity

The contemporary art world is conservatively estimated to be a $65 billion USD market that employs millions of human artists, sellers, and collectors globally. Recent attention paid to AI-made art in prestigious galleries, museums, and popular media has provoked debate around how these statistics will change. Unanswered questions fuel growing anxieties. Are AI-made and human-made art evaluated in the same ways? How will growing exposure to AI-made art impact evaluations of human creativity? Our research uses a psychological lens to explore these questions in the realm of visual art. We find that people devalue art labeled as AI-made across a variety of dimensions, even when they report it is indistinguishable from human-made art, and even when they believe it was produced collaboratively with a human. We also find that comparing images labeled as human-made to images labeled as AI-made increases perceptions of human creativity, an effect that can be leveraged to increase the value of human effort. Results are robust across six experiments (N = 2965) using a range of human-made and AI-made stimuli and incorporating representative samples of the US population. Finally, we highlight conditions that strengthen effects as well as dimensions where AI-devaluation effects are more pronounced.

Mind ascribed to AI and the appreciation of AI-generated art

Creative artificial intelligence (AI) has received a lot of attention in recent years. Artworks that are introduced to be generated by AI (rather than a human artist) are, however, often evaluated negatively. Integrating extant research, we suggest that AI is ascribed less mind (i.e. agency and experience) which is responsible for this effect. In two experiments ( N = 176 and N = 381) we observed negative indirect effects of artist information (AI vs human artist) on the appreciation of visual artworks. The AI is consistently ascribed less agency and less experience than a human artist. Higher levels of experience and agency ascribed to an artist are, in turn, associated with higher appreciation of a piece of art. In both experiments the total effect of artist information on appreciation was not significant. Artist information did not predict whether the artwork deviated positively from viewers’ expectations developed before the actual artwork was encountered.

IMPACT OF ARTIFICIAL INTELLIGENCE TECHNOLOGY IN FINE ART: IN REFERENCE OF PRINTMAKING

Artificial intelligence (AI) is revolutionizing the art industry. Fine craftsmen are using man-made intelligence to make craftsmanship pieces that are tastefully satisfying as well as accomplish remarkable structures and make forward-thinking workmanship viewpoints. Man-made intelligence innovation is currently turning into a significant device in the inventive flow, permitting capable craftsmen to grow their imaginative capacities and open doors. However, AI has also sparked debates regarding whether or not it can substitute for human artists. Through my studies, I look at how AI affects fine artists and their work as well as how AI might affect the art industry in the future.

Emojis Generation Based on Deep Convolution Generative Adversarial Network

With the development of information technology and mobile communication, people's usage of emoji is increasing. However, designing emojis by artists can be time-consuming and costly. Therefore, this study attempts to use the Deep Convolution Generative Adversarial Network (DCGAN) method in deep learning to automatically generate emojis. DCGAN is a combination of Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN). It introduces convolutional networks into the generative model for unsupervised training, which can improve the learning effect of the generator network. DCGAN consists of two different models: a generator model and a discriminator model. The generator's role is to produce fake images that are similar to the training images, and the discriminator's role is to judge whether the fake image is the same as a real image. During training, the generator continuously tries to defeat the discriminator by producing better and better fake images, while the discriminator tries its best to distinguish between real and fake images. Through continuous optimization of the generator and discriminator, this study can finally obtain clear new emojis. Overall, the images generated by DCGAN can largely fulfill our purpose. Many novel emojis could be obtained to replace the work of human artists. However, the quality of the generated emojis has some defects, and it is still necessary to screen out images with strange shapes that are not suitable for daily life.

Does an emotional connection to art really require a human artist? Emotion and intentionality responses to AI- versus human-created art and impact on aesthetic experience

AI has captured the artworld, and, and, progressively, is reshaping the way humans interact with various forms of media. Computer-generated art sells for millions at auctions; artists routinely use algorithms to generate aesthetic materials. However, to capture the impact of such works and our relationships with them, we need to better understand the kinds of responses we make to AI/computer-generated images. Here, we consider whether and, if so, to what extent humans report feeling emotions when engaging computer-generated art, or even ascribe intentionality behind those feelings. These are emerging—and also long-standing—points of controversy, with critical arguments that this should not occur, thus marking potential distinctions between artificial and ‘real’ human productions. We tested this by employing visually similar abstract, black-and-white artworks, made by a computer (RNG) or by human artists intentionally aiming at transmitting emotions. In a 2 × 2 design, participants (N = 48) viewed the art, preceded by primes about human/computer provenance (true, 50% of cases). Contrary to critical suggestions, participants almost always not only reported emotions but also ascribed intentionality, independent of the prime given. Interestingly, they did report stronger emotions when the work actually was made by a human. We discuss implications for our understanding of art engagements and future developments regarding computer-generated digital interactions.