The advancement of visual intelligence is intrinsically tethered to the availability of data. In parallel, generative Artificial Intelligence (AI) has unlocked the potential to create synthetic images that closely resemble real-world photographs, which prompts a compelling inquiry: how visual intelligence benefit from the advance of generative AI? This paper explores the innovative concept of harnessing these AI-generated images as a new data source, reshaping traditional model paradigms in visual intelligence. In contrast to real data, AI-generated data sources 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 examines 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数据的可获取性。与此同时，生成式人工智能（AI）已释放出创造与真实世界照片高度相似的合成图像的潜力，这引发了一个引人深思的问题：视觉智能如何从生成式AI的进步中获益？本文探索了利用这些AI生成图像作为新型数据源的创新概念，重塑了视觉智能中的传统模型范式。与真实数据相比，AI生成的数据源展现出显著优势，包括无与伦比的丰富性和可扩展性、快速生成大规模数据集的能力，以及轻松模拟边缘场景的特性。基于生成式AI模型取得的成功，我们检验了其生成数据在从训练机器学习模型到模拟计算建模、测试与验证场景等一系列应用中的潜力。我们探究了支撑这一开创性生成式AI应用的技术基础，并对伴随这一变革性范式转变的伦理、法律及实践考量进行了深入讨论。通过对当前技术与应用的全面调研，本文呈现了视觉智能合成纪元的全景视图。与本论文相关的项目可参见https://github.com/mwxely/AIGS。