Title:Semi-automatic Framework for Voxel Human Deformation Modeling
Volume: 20
Author(s): Yangchun Gao, Xu Xu, Congsheng Li, Jie Liu and Tongning Wu*
Affiliation:
- China Academy of Information and Communications Technology, Beijing 100191, China
Keywords:
Voxel human model, Surface reconstruction, Medical image deformation, Smoothed rotation enhanced ARAP, Laplace volume refilling, Volume model deformation.
Abstract:
Background:
With the advancement of computer and medical imaging technologies, a number of high-resolution, voxel-based, full-body human anatomical
models have been developed for medical education, industrial design, and physics simulation studies. However, these models are limited in many
applications because they are often only in an upstanding posture.
Objective:
To quickly develop multi-pose human models for different applications. A semi-automatic framework for voxel deformation is proposed in the
study.
Methods:
This paper describes a framework for human pose deformation based on three-dimensional (3D) medical images. The voxel model is first
converted into a surface model using a surface reconstruction algorithm. Second, a deformation skeleton based on human bones is defined, and the
surface model is bound to the skeleton. The bone Glow algorithm is used to assign weights to the surface vertices. Then, the model is deformed to
the target posture by using the Smoothed Rotation Enhanced As-Rigid-As-Possible (SR-ARAP) algorithm. Finally, the volume-filling algorithm is
applied to refill the tissues into the deformed surface model.
Results:
The proposed framework is used to deform two standing human models, and the sitting and running models are developed. The results show that
the framework can successfully develop the target pose. When compared to the results of the As-Rigid-As-Possible algorithm, SR-ARAP preserves
local tissues better.
Conclusion:
The study proposes a frame for voxel human model deformation and improves the local tissue integrity during deformation.
