GRCVHCAug 13, 2020

Motion Similarity Modeling -- A State of the Art Report

arXiv:2008.05872v1
Originality Synthesis-oriented
AI Analysis

It is a survey paper, so it is incremental, summarizing existing methods for researchers and practitioners in fields like robotics and animation.

This report tackles the problem of comparing human actions through similarity measures by providing an overview of motion analysis and various modeling methods, focusing on 3D motion data, but does not present new results or concrete numbers.

The analysis of human motion opens up a wide range of possibilities, such as realistic training simulations or authentic motions in robotics or animation. One of the problems underlying motion analysis is the meaningful comparison of actions based on similarity measures. Since the motion analysis is application-dependent, it is essential to find the appropriate motion similarity method for the particular use case. This state of the art report provides an overview of human motion analysis and different similarity modeling methods, while mainly focusing on approaches that work with 3D motion data. The survey summarizes various similarity aspects and features of motion and describes approaches to measuring the similarity between two actions.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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