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<p>AI-based video generation is rapidly advancing field with significant research focus and broad applications across industries like entertainment, education, and healthcare. To gain a structured understanding of the field, this paper systematically reviews the research dynamics and application trends in video generation from 2020 to April 2025. Utilizing a combined approach of content analysis and bibliometric analysis on 422 research publications selected from the Web of Science Core Collection database, we investigate key developments. The content analysis examines state-of-the-art algorithmic innovations, generation strategies, and prominent application domains. Bibliometric analysis, employing tools like CiteSpace and VOSviewer, maps publication trends, identifies influential authors, institutions, and sources, visualizes collaboration networks, and analyzes the evolution of research hotspots through keywords. Our findings the rise of specific architectures, pinpoint high-activity application areas, reveal evolving thematic clusters, and also remarks persistent challenges, including technical hurdles and critical ethical issues such as deepfake detection, algorithmic transparency, and data privacy. By synthesizing these analyses, this study offers a structured overview of the recent landscape, and highlights areas that may warrant further exploration in AI video generation.</p>

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Bibliometric analysis and review of AI-based video generation: research dynamics and application trends (2020–2025)

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月25日

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  • Wei Xie,
  • Anshu Hu,
  • Qing Xie,
  • Junjie Chen,
  • Ruoyu Wan,
  • Yuhan Liu

摘要

AI-based video generation is rapidly advancing field with significant research focus and broad applications across industries like entertainment, education, and healthcare. To gain a structured understanding of the field, this paper systematically reviews the research dynamics and application trends in video generation from 2020 to April 2025. Utilizing a combined approach of content analysis and bibliometric analysis on 422 research publications selected from the Web of Science Core Collection database, we investigate key developments. The content analysis examines state-of-the-art algorithmic innovations, generation strategies, and prominent application domains. Bibliometric analysis, employing tools like CiteSpace and VOSviewer, maps publication trends, identifies influential authors, institutions, and sources, visualizes collaboration networks, and analyzes the evolution of research hotspots through keywords. Our findings the rise of specific architectures, pinpoint high-activity application areas, reveal evolving thematic clusters, and also remarks persistent challenges, including technical hurdles and critical ethical issues such as deepfake detection, algorithmic transparency, and data privacy. By synthesizing these analyses, this study offers a structured overview of the recent landscape, and highlights areas that may warrant further exploration in AI video generation.