Summary of Establishing a Unified Evaluation Framework For Human Motion Generation: a Comparative Analysis Of Metrics, by Ali Ismail-fawaz and Maxime Devanne and Stefano Berretti and Jonathan Weber and Germain Forestier
Establishing a Unified Evaluation Framework for Human Motion Generation: A Comparative Analysis of Metrics
by Ali Ismail-Fawaz, Maxime Devanne, Stefano Berretti, Jonathan Weber, Germain Forestier
First submitted to arxiv on: 13 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the pressing need for a unified evaluation framework in generative artificial intelligence for human motion generation. The authors review eight existing evaluation metrics, highlighting their strengths and weaknesses. They propose standardized practices and introduce a new metric that assesses diversity in temporal distortion. The study also presents experimental results using three generative models on a publicly available dataset, providing insights into the interpretation of each metric. This work aims to provide a clear, user-friendly evaluation framework for newcomers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper discusses how there is no one-size-fits-all approach to evaluating human motion generation models. It highlights eight different metrics that have been used in previous studies, and explains what each metric measures. The authors then propose a new way of thinking about these metrics, and suggest some best practices for using them. They also do some experiments with three different models to show how the metrics work in practice. |
