Establishing a Unified Evaluation Framework for Human Motion Generation: A Comparative Analysis of Metrics
<|reference_start|>Establishing a Unified Evaluation Framework for Human Motion Generation: A Comparative Analysis of Metrics: 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.<|reference_end|>
@article{ismail-fawaz2024establishing,
    title={Establishing a Unified Evaluation Framework for Human Motion Generation:
  A Comparative Analysis of Metrics},
    author={Ali Ismail-Fawaz and Maxime Devanne and Stefano Berretti and Jonathan
  Weber and Germain Forestier},
    journal={arXiv preprint arXiv:2405.07680},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.07680},
    primaryClass={cs.CV cs.LG}
}