Do We Measure What We Perceive? Comparison of Perceptual and Computed Differences between Hand Animations
Do We Measure What We Perceive? Comparison of Perceptual and Computed Differences between Hand Animations
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DOI：
10.1145/3532719.3543233
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Jacob Justice;Alexndra Adkins;Tommy Dong;S. Jörg
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文献类型：
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作者：
Jacob Justice;Alexndra Adkins;Tommy Dong;S. Jörg
对公共运动捕捉数据的兴趣增加，允许通过神经网络使用数据驱动的动画算法。虽然运动捕捉数据越来越容易获得，但数据集已经变得太大，无法手动排序。相似性度量量化了两个运动的不同程度，与手动搜索相比，可以更快地搜索数据库以及训练神经网络。然而，最流行的相似性度量并不受人类感知的影响，这导致了感知上不相似的数据可能被这些度量标记为相似。我们进行了一个手部运动的实验，以确定人类感知和常见的相似性度量之间的差异有多大。在这项研究中，参与者观看了两个手部动作的动画，一个改变了，另一个没有改变，并在7分制的李克特量表上对它们的相似性进行了评分。在我们的比较中，我们发现没有一个测试的相似性度量与人类判断的相似性分数相关。
An increased interest in public motion capture data has allowed for the use of data-driven animation algorithms through neural networks. While motion capture data is increasingly accessible, data sets have become too large to sort through manually. Similarity metrics quantify how different two motions are and can be used to search databases much faster when compared to manual searches as well as to train neural networks. However, the most popular similarity metrics are not informed by human perception, resulting in the potential for data that is not perceptually similar being labeled as such by these metrics. We conducted an experiment with hand motions to identify how large the differences between human perception and common similarity metrics are. In this study, participants watched two animations of hand motions, one altered and the other unaltered, and scored their similarity on a 7-point Likert scale. In our comparisons, we found that none of the tested similarity metrics correlated with human judged scores of similarity.