Style Transfer: A Decade Survey

TL;DR

This survey comprehensively reviews a decade of AI-generated content in visual arts, highlighting key models like VAE, GANs, and Diffusion, and discusses their impact, limitations, and future challenges in creative technology.

Contribution

It provides a systematic review of over 500 papers, introduces a multidimensional evaluation framework, and offers a unified perspective on AI's role in artistic expression.

Findings

Affective impact of AI-generated art is profound.

Current models excel in visual quality but face limitations in creativity.

Future research should focus on enhancing artistic merit and computational efficiency.

Abstract

The revolutionary advancement of Artificial Intelligence Generated Content (AIGC) has fundamentally transformed the landscape of visual content creation and artistic expression. While remarkable progress has been made in image generation and style transfer, the underlying mechanisms and aesthetic implications of these technologies remain insufficiently understood. This paper presents a comprehensive survey of AIGC technologies in visual arts, tracing their evolution from early algorithmic frameworks to contemporary deep generative models. We identify three pivotal paradigms: Variational Autoencoders (VAE), Generative Adversarial Networks (GANs), and Diffusion Models, and examine their roles in bridging the gap between human creativity and machine synthesis. To support our analysis, we systematically review over 500 research papers published in the past decade, spanning both foundational developments and state-of-the-art innovations. Furthermore, we propose a multidimensional evaluation framework that incorporates Technical Innovation, Artistic Merit, Visual Quality, Computational Efficiency, and Creative Potential. Our findings reveal both the transformative capacities and current limitations of AIGC systems, emphasizing their profound impact on the future of creative practices. Through this extensive synthesis, we offer a unified perspective on the convergence of artificial intelligence and artistic expression, while outlining key challenges and promising directions for future research in this rapidly evolving field.