# A Survey of AI-Generated Video Evaluation

arXiv: https://arxiv.org/abs/2410.19884 · HTML: https://mlsanity.com/paper/2410.19884/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2024.

## Built on by (most cited first, 17 of 17)

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- [Human-Activity AGV Quality Assessment: A Benchmark Dataset and an Objective Evaluation Metric](https://mlsanity.com/paper/2411.16619/) (2024)
- [ETVA: Evaluation of Text-to-Video Alignment via Fine-grained Question Generation and Answering](https://mlsanity.com/paper/2503.16867/) (2025)
- [AIGVE-Tool: AI-Generated Video Evaluation Toolkit with Multifaceted Benchmark](https://mlsanity.com/paper/2503.14064/) (2025)
- [AEGIS: Authenticity Evaluation Benchmark for AI-Generated Video Sequences](https://mlsanity.com/paper/2508.10771/) (2025)
- [AIGVE-MACS: Unified Multi-Aspect Commenting and Scoring Model for AI-Generated Video Evaluation](https://mlsanity.com/paper/2507.01255/) (2025)
- [Video Models Start to Solve Chess, Maze, Sudoku, Mental Rotation, and Raven' Matrices](https://mlsanity.com/paper/2512.05969/) (2025)
- [A Survey: Spatiotemporal Consistency in Video Generation](https://mlsanity.com/paper/2502.17863/) (2025)
- [Are AI-Generated Driving Videos Ready for Autonomous Driving? A Diagnostic Evaluation Framework](https://mlsanity.com/paper/2512.06376/) (2025)
- [OccFace: Unified Occlusion-Aware Facial Landmark Detection with Per-Point Visibility](https://mlsanity.com/paper/2602.10728/) (2026)
- [Illusion or Integrity? Geometrical Consistency Metric for AIGC Video Quality Evaluation](https://mlsanity.com/paper/2608.09594/) (2026)
- [Are Video Generation Models Geographically Fair? An Attraction-Centric Evaluation of Global Visual Knowledge](https://mlsanity.com/paper/2601.18698/) (2026)
- [Group-of-Latents: Perceptual Video Compression at Extreme Bitrates via Masked Latent Generative Modeling](https://mlsanity.com/paper/2607.19437/) (2026)

## Abstract (arXiv)

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.