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.

翻译：生成式人工智能近年来取得了显著进展，并在计算机视觉、计算设计等不同领域的各类生成任务中展现了卓越性能。许多研究者尝试将生成式人工智能集成到可视化框架中，利用其强大的生成能力支持不同操作。同时，扩散模型和大语言模型等生成式人工智能的重大突破也极大提升了生成式人工智能驱动可视化（GenAI4VIS）的潜力。本文从技术视角回顾了以往基于生成式人工智能的可视化研究，并探讨了未来研究的挑战与机遇。具体而言，我们总结了序列、表格、空间和图生成技术等不同类型生成式人工智能方法在可视化任务中的应用，并将其归纳为四个主要阶段：数据增强、视觉映射生成、风格化与交互。针对每个具体可视化子任务，我们阐释了典型数据与具体生成式人工智能算法，旨在深入理解当前最先进的GenAI4VIS技术及其局限性。此外，基于调研结果，我们讨论了评估、数据集以及端到端生成式人工智能与生成算法之间差距这三个主要方面的挑战与研究机遇。通过总结不同生成算法及其当前应用与局限性，本文旨在为未来的GenAI4VIS研究提供有益启示。