Recently, Artificial Intelligence Generated Content (AIGC) has gained significant attention from society, especially with the rise of Generative AI (GAI) techniques such as ChatGPT, GPT-4 [165], DALL-E-3 [184], and Sora [137]. AIGC involves using AI models to create digital content, such as images, music, and natural language, with the goal of making the content creation process more efficient and accessible. Large-scale models have become increasingly important in AIGC as they provide better intent extraction and generation results. This survey provides a comprehensive review of the history of generative models and recent advances in AIGC, focusing on both unimodal and multimodal interaction. From the perspective of unimodality, we introduce the generation tasks and relative models of text and image. From the perspective of multimodality, we introduce the cross-application between the modalities mentioned above. Finally, the survey discusses the existing open problems and future challenges in AIGC. Overall, this survey serves as a valuable resource for individuals interested in understanding the background and secrets behind the impressive performance of AIGC techniques.

中文翻译：

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AI 生成内容调查 （AIGC）


近年来，人工智能生成内容 （AIGC） 受到了社会的广泛关注，尤其是随着 ChatGPT、GPT-4 [165]、DALL-E-3 [184] 和 Sora [137] 等生成式 AI （GAI） 技术的兴起。AIGC 涉及使用 AI 模型创建数字内容，例如图像、音乐和自然语言，目的是使内容创建过程更加高效和可访问。大规模模型在 AIGC 中变得越来越重要，因为它们提供了更好的意图提取和生成结果。本调查全面回顾了生成模型的历史和 AIGC 的最新进展，重点关注单峰和多峰交互。从单模态的角度，我们介绍了文本和图像的生成任务和相对模型。从多模态的角度出发，我们介绍了上述模态之间的交叉应用。最后，该调查讨论了 AIGC 中现有的未解决的问题和未来的挑战。总的来说，对于有兴趣了解 AIGC 技术令人印象深刻的性能背后的背景和秘密的个人来说，这项调查是宝贵的资源。