Correlation as a measure for alignment and similarity of human motions

Randall, M ORCID: https://orcid.org/0009-0000-2869-1008, Harvey, C ORCID: https://orcid.org/0000-0002-4809-1592 and Williams, I ORCID: https://orcid.org/0000-0002-0651-0963 (2023) Correlation as a measure for alignment and similarity of human motions. Computer Animation and Virtual Worlds, 34 (3-4). e2157. ISSN 1546-4261

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Abstract

The ability to measure similarity and alignment of motions is a key tool in motion retrieval and motion editing. Similarity metrics based on distance functions are often utilized when measuring similarity of human motions, however, metrics based on correlation can also potentially useful for measuring similarity and alignment. This paper evaluates the use of correlation as a method of measuring the alignment and similarity of human motion and compares them against more established distance-based metrics. Three correlation methods and five methods of parameterising rotation are evaluated. The results show that parameterization based on displacement vectors and Kendall Tau rank correlation are optimal for measuring the alignment between two motions. If measuring similarity of motions, however, an approach based on distance metrics for angular or positional distance should be used.