A Survey of AI-Generated Video Evaluation
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911530616881152 |
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| author | Liu, Xiao Xiang, Xinhao Li, Zizhong Wang, Yongheng Li, Zhuoheng Liu, Zhuosheng Zhang, Weidi Ye, Weiqi Zhang, Jiawei |
| author_facet | Liu, Xiao Xiang, Xinhao Li, Zizhong Wang, Yongheng Li, Zhuoheng Liu, Zhuosheng Zhang, Weidi Ye, Weiqi Zhang, Jiawei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19884 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Survey of AI-Generated Video Evaluation Liu, Xiao Xiang, Xinhao Li, Zizhong Wang, Yongheng Li, Zhuoheng Liu, Zhuosheng Zhang, Weidi Ye, Weiqi Zhang, Jiawei Computer Vision and Pattern Recognition 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. |
| title | A Survey of AI-Generated Video Evaluation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.19884 |