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Adaptable Anatomical Models for Realistic Bone Motion Reconstruction
Author(s) -
Zhu Lifeng,
Hu Xiaoyan,
Kavan Ladislav
Publication year - 2015
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12575
Subject(s) - motion capture , computer science , kinematics , computer vision , artificial intelligence , point cloud , inverse kinematics , motion (physics) , skinning , joint (building) , computer graphics (images) , point (geometry) , anatomy , mathematics , geometry , medicine , architectural engineering , physics , classical mechanics , robot , engineering
We present a system to reconstruct subject‐specific anatomy models while relying only on exterior measurements represented by point clouds. Our model combines geometry, kinematics, and skin deformations (skinning). This joint model can be adapted to different individuals without breaking its functionality, i.e., the bones and the skin remain well‐articulated after the adaptation. We propose an optimization algorithm which learns the subject‐specific (anthropometric) parameters from input point clouds captured using commodity depth cameras. The resulting personalized models can be used to reconstruct motion of human subjects. We validate our approach for upper and lower limbs, using both synthetic data and recordings of three different human subjects. Our reconstructed bone motion is comparable to results obtained by optical motion capture (Vicon) combined with anatomically‐based inverse kinematics (OpenSIM). We demonstrate that our adapted models better preserve the joint structure than previous methods such as OpenSIM or Anatomy Transfer.