Evaluating the Evaluators: Towards Human-aligned Metrics for Missing Markers Reconstruction
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909876908720128 |
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| author | Kucherenko, Taras Peristy, Derek Bütepage, Judith |
| author_facet | Kucherenko, Taras Peristy, Derek Bütepage, Judith |
| contents | Animation data is often obtained through optical motion capture systems, which utilize a multitude of cameras to establish the position of optical markers. However, system errors or occlusions can result in missing markers, the manual cleaning of which can be time-consuming. This has sparked interest in machine learning-based solutions for missing marker reconstruction in the academic community. Most academic papers utilize a simplistic mean square error as the main metric. In this paper, we show that this metric does not correlate with subjective perception of the fill quality. Additionally, we introduce and evaluate a set of better-correlated metrics that can drive progress in the field. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_14334 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluating the Evaluators: Towards Human-aligned Metrics for Missing Markers Reconstruction Kucherenko, Taras Peristy, Derek Bütepage, Judith Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning Animation data is often obtained through optical motion capture systems, which utilize a multitude of cameras to establish the position of optical markers. However, system errors or occlusions can result in missing markers, the manual cleaning of which can be time-consuming. This has sparked interest in machine learning-based solutions for missing marker reconstruction in the academic community. Most academic papers utilize a simplistic mean square error as the main metric. In this paper, we show that this metric does not correlate with subjective perception of the fill quality. Additionally, we introduce and evaluate a set of better-correlated metrics that can drive progress in the field. |
| title | Evaluating the Evaluators: Towards Human-aligned Metrics for Missing Markers Reconstruction |
| topic | Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2410.14334 |