Article Dans Une Revue Computer Vision and Image Understanding Année : 2025

Establishing a unified evaluation framework for human motion generation: A comparative analysis of metrics

Résumé

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 two publicly available datasets, 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: https://github.com/MSD-IRIMAS/Evaluating-HMG.

Fichier principal
Vignette du fichier
cviu2025.pdf (1.78 Mo) Télécharger le fichier
Origine Publication financée par une institution
Licence

Dates et versions

hal-05142916 , version 1 (03-07-2025)

Licence

Identifiants

Citer

Ali Ismail-Fawaz, Maxime Devanne, Stefano Berretti, Jonathan Weber, Germain Forestier. Establishing a unified evaluation framework for human motion generation: A comparative analysis of metrics. Computer Vision and Image Understanding, 2025, 254, pp.104337. ⟨10.1016/j.cviu.2025.104337⟩. ⟨hal-05142916⟩
133 Consultations
148 Téléchargements

Altmetric

Partager

  • More