The development of generative artificial intelligence for human motion
generation has expanded rapidly, necessitating a unified evaluation framework.
This paper presents a detailed review of eight evaluation metrics for human
motion generation, highlighting their unique features and shortcomings. We
propose standardized practices through a unified evaluation setup to facilitate
consistent model comparisons. Additionally, we introduce a novel metric that
assesses diversity in temporal distortion by analyzing warping diversity,
thereby enhancing the evaluation of temporal data. We also conduct experimental
analyses of three generative models using a publicly available dataset,
offering insights into the interpretation of each metric in specific case
scenarios. Our goal is to offer a clear, user-friendly evaluation framework for
newcomers, complemented by publicly accessible code.

发展用于人类动作生成的生成式人工智能迅速扩展，需要一个统一的评估框架。本文详细评述了八种用于人类动作生成的评估指标，突出其独特特点和不足之处。我们提出了一种标准化实践，通过一个统一的评估设置来促进一致的模型比较。此外，我们引入了一种新的度量标准，通过分析时间扭曲的多样性来评估时间数据的多样性，从而增强了时间数据的评估能力。我们还使用一个公开可用的数据集对三个生成模型进行了实验分析，提供了在特定案例场景中对每个度量标准的解释。我们的目标是为初学者提供一个清晰、用户友好的评估框架，并附带公开可访问的代码。