The advancement of visual intelligence is intrinsically tethered to the availability of large-scale data. In parallel, generative Artificial Intelligence (AI) has unlocked the potential to create synthetic images that closely resemble real-world photographs. This prompts a compelling inquiry: how much visual intelligence could benefit from the advance of generative AI? This paper explores the innovative concept of harnessing these AI-generated images as new data sources, reshaping traditional modeling paradigms in visual intelligence. In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability, the rapid generation of vast datasets, and the effortless simulation of edge cases. Built on the success of generative AI models, we examine the potential of their generated data in a range of applications, from training machine learning models to simulating scenarios for computational modeling, testing, and validation. We probe the technological foundations that support this groundbreaking use of generative AI, engaging in an in-depth discussion on the ethical, legal, and practical considerations that accompany this transformative paradigm shift. Through an exhaustive survey of current technologies and applications, this paper presents a comprehensive view of the synthetic era in visual intelligence. A project associated with this paper can be found at https://github.com/mwxely/AIGS .

翻译：视觉智能的进步本质上依赖于大规模数据的可用性。与此同时，生成式人工智能（AI）已释放出创造与真实照片高度相似的合成图像的潜力。这引发了一个引人探究的问题：生成式AI的进步能在多大程度上惠及视觉智能？本文探索了将AI生成图像作为新数据源的创新概念，重塑视觉智能中的传统建模范式。与真实数据相比，AI生成数据展现出显著优势，包括无与伦比的丰富性和可扩展性、海量数据集的快速生成，以及边缘案例的轻松模拟。基于生成式AI模型的成功，我们考察了其生成数据在从训练机器学习模型到为计算建模、测试和验证模拟场景等一系列应用中的潜力。我们探讨了支撑生成式AI这一突破性应用的技术基础，并就伴随这一变革性范式转变的伦理、法律和实践考量进行了深入讨论。通过对当前技术和应用的全面调查，本文提供了视觉智能合成时代的全面视角。与本文相关的项目可在https://github.com/mwxely/AIGS 获取。