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Recently, ChatGPT, along with DALL-E-2 and Codex,has been gaining significantattention from society. As a result, many individuals have become interested inrelated resources and are seeking to uncover the background and secrets behindits impressive performance. In fact, ChatGPT and other Generative AI (GAI)techniques belong to the category of Artificial Intelligence Generated Content(AIGC), which involves the creation of digital content, such as images, music,and natural language, through AI models. The goal of AIGC is to make thecontent creation process more efficient and accessible, allowing for theproduction of high-quality content at a faster pace. AIGC is achieved byextracting and understanding intent information from instructions provided byhuman, and generating the content according to its knowledge and the intentinformation. In recent years, large-scale models have become increasinglyimportant in AIGC as they provide better intent extraction and thus, improvedgeneration results. With the growth of data and the size of the models, thedistribution that the model can learn becomes more comprehensive and closer toreality, leading to more realistic and high-quality content generation. Thissurvey provides a comprehensive review on the history of generative models, andbasic components, recent advances in AIGC from unimodal interaction andmultimodal interaction. From the perspective of unimodality, we introduce thegeneration tasks and relative models of text and image. From the perspective ofmultimodality, we introduce the cross-application between the modalitiesmentioned above. Finally, we discuss the existing open problems and futurechallenges in AIGC.

A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT