Recent advances in generative modeling can create remarkably realistic synthetic videos, making it increasingly difficult for humans to distinguish them from real ones and necessitating reliable detection methods. However, two key limitations hinder the development of this field. \textbf{From the dataset perspective}, existing datasets are often limited in scale and constructed using outdated or narrowly scoped generative models, making it difficult to capture the diversity and rapid evolution of modern generative techniques. Moreover, the dataset construction process frequently prioritizes quantity over quality, neglecting essential aspects such as semantic diversity, scenario coverage, and technological representativeness. \textbf{From the benchmark perspective}, current benchmarks largely remain at the stage of dataset creation, leaving many fundamental issues and in-depth analysis yet to be systematically explored. Addressing this gap, we propose AIGVDBench, a benchmark designed to be comprehensive and representative, covering \textbf{31} state-of-the-art generation models and over \textbf{440,000} videos. By executing more than \textbf{1,500} evaluations on \textbf{33} existing detectors belonging to four distinct categories. This work presents \textbf{8 in-depth analyses} from multiple perspectives and identifies \textbf{4 novel findings} that offer valuable insights for future research. We hope this work provides a solid foundation for advancing the field of AI-generated video detection. Our benchmark is open-sourced at https://github.com/LongMa-2025/AIGVDBench.

翻译：生成建模的最新进展能够创建极为逼真的合成视频，使得人类越来越难以将其与真实视频区分开来，从而需要可靠的检测方法。然而，该领域的发展受到两个关键限制的阻碍。\textbf{从数据集的角度看}，现有数据集通常规模有限，并且使用过时或范围狭窄的生成模型构建，难以捕捉现代生成技术的多样性和快速演变。此外，数据集构建过程常常优先考虑数量而非质量，忽视了语义多样性、场景覆盖和技术代表性等基本方面。\textbf{从基准测试的角度看}，当前的基准测试很大程度上仍停留在数据集创建阶段，许多基本问题和深入分析尚未得到系统探索。为弥补这一空白，我们提出了AIGVDBench，这是一个旨在全面且具有代表性的基准测试，涵盖了\textbf{31}个最先进的生成模型和超过\textbf{440,000}个视频。通过对属于四个不同类别的\textbf{33}个现有检测器执行超过\textbf{1,500}次评估，本工作从多个角度提出了\textbf{8项深入分析}，并识别出\textbf{4项新颖发现}，为未来研究提供了宝贵的见解。我们希望这项工作能为推进AI生成视频检测领域奠定坚实的基础。我们的基准测试已在https://github.com/LongMa-2025/AIGVDBench开源。