Point-Cloud Self-Adaptive Pose Transfer Based on Skinning Deformation

Abstract

A point cloud pose transfer method based on self-adaptive weights skinning deformation is proposed to avoid the problems such as model tearing, distortion and insufficient pose transfer caused by the global smoothing coefficients of DDM. Firstly, the isomorphic skeletons of the source point cloud and the reference point cloud are extracted through the improved Laplacian contraction skeleton extraction method, the locations of the joints are optimized via clustering, and the geometric transformations of the joints between two isomorphic skeletons are calculated according to the isomorphic skeletons. Then, the per-vertex smoothing weights of DDM are improved based on the deformation degree of the vertex and clustering partition, and the rigid skinning weights of the vertices of source model are determined by using the skeleton hierarchy information. Finally, pose transfer is achieved via reformulating skinning as solving the rigid transformation matrix. Skeleton extraction and skinning deformation are carried out on the existing human point cloud models from MPI DYNA and animal point cloud models from MIT. The isomorphic skeletons generated by the proposed method are without redundant branches and joints. The experiment results show that the reference pose is studied sufficiently and the details of the source are kept well.