Landmark-Guided Elastic Shape Analysis of Human Character Motions

TL;DR

This paper introduces a shape analysis approach for human motion animations, enhancing temporal alignment by integrating feature points into elastic metrics, aiding in motion processing, interpolation, and classification.

Contribution

It extends classical elastic shape analysis by incorporating feature points for better alignment of human motion animations.

Findings

Improved temporal alignment of animations.

Enhanced motion interpolation and classification.

Effective integration of feature points into elastic metrics.

Abstract

Motions of virtual characters in movies or video games are typically generated by recording actors using motion capturing methods. Animations generated this way often need postprocessing, such as improving the periodicity of cyclic animations or generating entirely new motions by interpolation of existing ones. Furthermore, search and classification of recorded motions becomes more and more important as the amount of recorded motion data grows. In this paper, we will apply methods from shape analysis to the processing of animations. More precisely, we will use the by now classical elastic metric model used in shape matching, and extend it by incorporating additional inexact feature point information, which leads to an improved temporal alignment of different animations.