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Motion capture data segmentation using Riemannian manifold learning
Author(s) -
Bin Wang,
Weibin Liu,
Weiwei Xing
Publication year - 2019
Publication title -
computer animation and virtual worlds
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.225
H-Index - 49
eISSN - 1546-427X
pISSN - 1546-4261
DOI - 10.1002/cav.1885
Subject(s) - geodesic , manifold (fluid mechanics) , quaternion , segmentation , nonlinear dimensionality reduction , computer science , riemannian manifold , euclidean space , motion (physics) , artificial intelligence , manifold alignment , computer vision , algorithm , mathematics , mathematical analysis , geometry , dimensionality reduction , mechanical engineering , engineering
Abstract Due to the inherent nonlinear nature of data, traditional linear methods have some limitations in finding the intrinsic dimensions of motion capture (Mo‐cap) data. Mo‐cap data are more in line with the characteristics of the manifold. Assuming that the data are initially a low‐dimensional manifold and uniformly sampled in high‐dimensional Euclidean space, manifold learning recovers low‐dimensional manifold structures from high‐dimensional sampled data. This paper proposes an automatic segmentation method based on geodesics by introducing a Riemannian manifold. We convert Mo‐cap data from Euler angles into quaternions, calculate the intrinsic mean of the motion sequence, hemispherize quaternions, and use logarithmic and exponential mapping to calculate geodesic distances instead of quaternions. The experimental results show that the algorithms can achieve automatic segmentation and have a better segmentation effect.