  Recently, ChatGPT, along with DALL-E-2 and Codex,has been gaining significant
attention from society. As a result, many individuals have become interested in
related resources and are seeking to uncover the background and secrets behind
its 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 the
content creation process more efficient and accessible, allowing for the
production of high-quality content at a faster pace. AIGC is achieved by
extracting and understanding intent information from instructions provided by
human, and generating the content according to its knowledge and the intent
information. In recent years, large-scale models have become increasingly
important in AIGC as they provide better intent extraction and thus, improved
generation results. With the growth of data and the size of the models, the
distribution that the model can learn becomes more comprehensive and closer to
reality, leading to more realistic and high-quality content generation. This
survey provides a comprehensive review on the history of generative models, and
basic components, recent advances in AIGC from unimodal interaction 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, we discuss the existing open problems and future
challenges in AIGC.
