Generative AI (GenAI) has witnessed remarkable progress in recent years and
demonstrated impressive performance in various generation tasks in different
domains such as computer vision and computational design. Many researchers have
attempted to integrate GenAI into visualization framework, leveraging the
superior generative capacity for different operations. Concurrently, recent
major breakthroughs in GenAI like diffusion model and large language model have
also drastically increase the potential of GenAI4VIS. From a technical
perspective, this paper looks back on previous visualization studies leveraging
GenAI and discusses the challenges and opportunities for future research.
Specifically, we cover the applications of different types of GenAI methods
including sequence, tabular, spatial and graph generation techniques for
different tasks of visualization which we summarize into four major stages:
data enhancement, visual mapping generation, stylization and interaction. For
each specific visualization sub-task, we illustrate the typical data and
concrete GenAI algorithms, aiming to provide in-depth understanding of the
state-of-the-art GenAI4VIS techniques and their limitations. Furthermore, based
on the survey, we discuss three major aspects of challenges and research
opportunities including evaluation, dataset, and the gap between end-to-end
GenAI and generative algorithms. By summarizing different generation
algorithms, their current applications and limitations, this paper endeavors to
provide useful insights for future GenAI4VIS research.

本文总结了在可视化领域中利用生成式人工智能（GenAI）的方法，涵盖了不同类型的 GenAI 方法在数据增强、可视化映射生成、风格化和交互等各个阶段的应用，同时阐述了评估、数据集和端到端 GenAI 与生成算法之间的挑战和研究机会。通过总结不同的生成算法、其当前应用和限制，希望为未来的 GenAI4VIS 研究提供有用的见解。