## 2 Methodology  
To conduct our research, we conducted an extensive literature search across multiple scholarly databases, including Google Scholar, Semantic Scholar, ACM Digital Library, and IEEE Xplore. Our search was based on a carefully crafted search string \"(\"Generative AI\" OR \"AIGC\" OR \"Generative Adversarial Networks\" OR \"GANs\" OR \"Generative Models\") AND (\"survey\" OR \"review\" OR \"overview\" OR \"summary\" OR \"literature review\")\" designed to capture relevant studies pertaining to our research questions. We meticulously reviewed the search results and included studies that addressed our research inquiries directly. As part of our inclusion criteria, we focused on papers published in English, ensuring consistency in language and accessibility. We also took measures to eliminate duplicate papers, carefully screened the abstracts and conclusions of the selected papers, and evaluated their suitability based on qualitative or quantitative analysis, surveys, or review papers. Additionally, we identified valuable secondary references through survey papers to augment our research findings. Subsequently, we extracted and synthesized pertinent data attributes aligned with our research questions, enabling us to present comprehensive and insightful results. The search process results are as follows: The publication years range from 2014 to the present, with 90% of the papers published from 2017 to the present. The year-wise publication count is given in Table 11. This indicates that more relevant papers have been published in recent times. We have extracted data from 122 full-text papers, which consist of peer-reviewed publications in conferences and journals and accepted articles archived in the arXiv database. Among these articles, 49% are from conference proceedings, while 51% are from journals and archives. The distribution of the publication type is given in Table 12. Notably, we found 15 papers at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), followed by ten papers from the International Conference on Machine Learning and eight papers published in the Advances in Neural Information Processing Systems and seven papers in IEEE International Conference on Computer Vision (ICCV). The remaining papers are in the following order: European Conference on Computer Vision, International Conference on Multimedia, International Conference on Learning Representations (ICLR), International Conference on Neural Information Processing Systems, Conference on Empirical Methods in Natural Language Processing, etc. The other venues are listed in Table 13.  
Additionally, we identified 29 survey or literature review papers apart from the aforementioned papers. We have separated the survey papers and analyzed them based on our research questions, comparing their contributions with our own in this paper. We  
Figure 1: Organization of this review paper. have labeled the contributions as L, M, H, or NA, representing low, medium, high, and not applicable, respectively. We observed that the majority of the surveys did not explicitly focus on the requirements and evaluation metrics of AIGC. We summarized the important survey papers in Table 2 and compared them with our paper. The table provides a comprehensive overview of various papers and their contributions to the field of generative AI. It includes papers published between 2017 and 2023, as well as the contributions of each paper in terms of AIGC requirements taxonomy, AIGC models, AIGC input-output classification, and AIGC evaluation metric classification. Most papers reviewed (labeled with \"L\") focus on different aspects of AIGC, such as the applications of GANs across various domains and the progress made in computer vision using GANs. Some papers (labeled with \"M\" or \"H\") provide more in-depth analyses of AIGC models and their variants, particularly in the fields of drug discovery, material science, and speech synthesis.