Copying style, Extracting value: Illustrators' Perception of AI Style Transfer and its Impact on Creative Labor

2024-09-25

arXiv.org

AI Summary

This study investigates the impact of AI image generation tools on illustrators’ creative labor, focusing on tensions between technical capabilities of style transfer and professional conceptions of artistic authorship. Method: We fine-tuned Stable Diffusion models for four professional illustrators and conducted in-depth qualitative interviews, analyzed through the lens of boundary object theory to examine how creators perceive and negotiate AI-mediated style reproduction. Contribution/Results: We find that current AI systems replicate only superficial aesthetic features, failing to disentangle content from style and lacking emergent stylistic properties and contextual situatedness. Illustrators consistently perceive others’ styles as more readily imitable than their own—highlighting risks of technological misuse and devaluation of creative labor. The study innovatively reframes style transfer not as artistic substitution but as a supply-chain optimization technology, thereby extending critical analytical frameworks in Human–Computer Interaction (HCI) for AI-augmented creative practice.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Philosophy and Ethics of AI: AI & Jobs/Labor

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Generative text-to-image models are disrupting the lives of creative professionals. Specifically, illustrators are threatened by models that claim to extract and reproduce their style. Yet, research on style transfer has rarely focused on their perspectives. We provided four illustrators with a model fine-tuned to their style and conducted semi-structured interviews about the model's successes, limitations, and potential uses. Evaluating their output, artists reported that style transfer successfully copies aesthetic fragments but is limited by content-style disentanglement and lacks the crucial emergent quality of their style. They also deemed the others' copies more successful. Understanding the results of style transfer as"boundary objects,"we analyze how they can simultaneously be considered unsuccessful by artists and poised to replace their work by others. We connect our findings to critical HCI frameworks, demonstrating that style transfer, rather than merely a Creativity Support Tool, should also be understood as a supply chain optimization one.

Problem

• AI Image Generation
• Illustrator Perception
• Artistic Style Imitation

Innovation

• AI-generated art
• creative process impact
• computer-assisted design enhancement