  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.
