Establishing a Unified Evaluation Framework for Human Motion Generation: A Comparative Analysis of Metrics

• URL: http://arxiv.org/abs/2405.07680v1
• Date: Mon, 13 May 2024 12:10:57 GMT
• Title: Establishing a Unified Evaluation Framework for Human Motion Generation: A Comparative Analysis of Metrics
• Authors: Ali Ismail-Fawaz, Maxime Devanne, Stefano Berretti, Jonathan Weber, Germain Forestier,
• Abstract summary: This paper presents a detailed review of eight evaluation metrics for human motion generation. We propose standardized practices through a unified evaluation setup to facilitate consistent model comparisons. We introduce a novel metric that assesses diversity in temporal distortion by analyzing warping diversity.
• Score: 6.708543240320757
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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