Biharmonic 3D Parametric Human Model

Abstract

3D human body parametric model is a kind of high-level semantic information, which can provide effective prior knowledge for 3D human body reconstruction. The existing human body parametric model does not consider the local similarity characteristics of parameters, and it is difficult to guarantee the smooth model. A 3D human body parametric model based on the BlendSCAPE model is proposed. The biharmonic constraint is considered in every deformation step of the model. Then, each parameter is trained in a data-driven way, and the whole training process is redesigned with the introduction of biharmonic constraint. The training of parameters is finished by solving some minimum energy functions. Finally, experiments are carried out on CAESAR datasets. The results show that the model has a 14.2% reduction in the real data fitting error compared with BlendSCAPE model. Through generating human shapes with different poses and shapes and comparing the mesh before and after Laplace smoothing, the smoothness of the model is improved by 7.3%. The pose estimation results of real data in A-pose show that the deviation of the body part of the model is reduced by 18.6%.