Diffusion-Based Visual Art Creation: A Survey and New Perspectives

TL;DR

This survey reviews the development of diffusion-based generative AI techniques in visual art, analyzing their technical and artistic aspects, and discusses future directions for integrating AI with creative processes.

Contribution

It provides a comprehensive overview of diffusion-based visual art creation, highlighting technical challenges, design principles, and future perspectives in this emerging interdisciplinary field.

Findings

Diffusion methods are transforming artistic creation processes.

Technical challenges include data features and framework design.

Future directions emphasize synergy between AI and human creativity.

Abstract

The integration of generative AI in visual art has revolutionized not only how visual content is created but also how AI interacts with and reflects the underlying domain knowledge. This survey explores the emerging realm of diffusion-based visual art creation, examining its development from both artistic and technical perspectives. We structure the survey into three phases, data feature and framework identification, detailed analyses using a structured coding process, and open-ended prospective outlooks. Our findings reveal how artistic requirements are transformed into technical challenges and highlight the design and application of diffusion-based methods within visual art creation. We also provide insights into future directions from technical and synergistic perspectives, suggesting that the confluence of generative AI and art has shifted the creative paradigm and opened up new possibilities. By summarizing the development and trends of this emerging interdisciplinary area, we aim to shed light on the mechanisms through which AI systems emulate and possibly, enhance human capacities in artistic perception and creativity.