Establishing a Unified Evaluation Framework for Human Motion Generation: A Comparative Analysis of Metrics
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866913348220616704 |
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| author | Ismail-Fawaz, Ali Devanne, Maxime Berretti, Stefano Weber, Jonathan Forestier, Germain |
| author_facet | Ismail-Fawaz, Ali Devanne, Maxime Berretti, Stefano Weber, Jonathan Forestier, Germain |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07680 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Establishing a Unified Evaluation Framework for Human Motion Generation: A Comparative Analysis of Metrics Ismail-Fawaz, Ali Devanne, Maxime Berretti, Stefano Weber, Jonathan Forestier, Germain Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Establishing a Unified Evaluation Framework for Human Motion Generation: A Comparative Analysis of Metrics |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2405.07680 |