A Survey of AI-Generated Video Evaluation

• URL: http://arxiv.org/abs/2410.19884v1
• Date: Thu, 24 Oct 2024 23:08:39 GMT
• Title: A Survey of AI-Generated Video Evaluation
• Authors: Xiao Liu, Xinhao Xiang, Zizhong Li, Yongheng Wang, Zhuoheng Li, Zhuosheng Liu, Weidi Zhang, Weiqi Ye, Jiawei Zhang,
• Abstract summary: This survey identifies the emerging field of AI-Generated Video Evaluation (AIGVE) We advocate for the development of more robust and nuanced evaluation frameworks that can handle the complexities of video content. This survey aims to establish a foundational knowledge base for both researchers from academia and practitioners from the industry.
• Score: 9.100408575312281
• License:
• Abstract: The growing capabilities of AI in generating video content have brought forward significant challenges in effectively evaluating these videos. Unlike static images or text, video content involves complex spatial and temporal dynamics which may require a more comprehensive and systematic evaluation of its contents in aspects like video presentation quality, semantic information delivery, alignment with human intentions, and the virtual-reality consistency with our physical world. This survey identifies the emerging field of AI-Generated Video Evaluation (AIGVE), highlighting the importance of assessing how well AI-generated videos align with human perception and meet specific instructions. We provide a structured analysis of existing methodologies that could be potentially used to evaluate AI-generated videos. By outlining the strengths and gaps in current approaches, we advocate for the development of more robust and nuanced evaluation frameworks that can handle the complexities of video content, which include not only the conventional metric-based evaluations, but also the current human-involved evaluations, and the future model-centered evaluations. This survey aims to establish a foundational knowledge base for both researchers from academia and practitioners from the industry, facilitating the future advancement of evaluation methods for AI-generated video content.

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