Adaptable Anatomical Models for Realistic Bone Motion Reconstruction

Lifeng Zhu, Xiaoyan Hu, Ladislav Kavan
2015 Computer graphics forum (Print)  
b) (c) Geometry (f) (a) (e) Skinning (d) Kinematics Figure 1: Our system starts by capturing animated point clouds using commodity depth-sensors (a). These point clouds (b) are used to learn the subject-specific parameters of our anatomical model, which consists of geometry (c), kinematics (d), and skinning (e). We apply our final personalized model to reconstruct anatomically-plausible bone motion(f). Abstract We present a system to reconstruct subject-specific anatomy models while relying
more » ... 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.
doi:10.1111/cgf.12575 fatcat:teimaed2cjbwfayj6alr7y7scy