3D mesh pose transfer based on skeletal deformation

Abstract

For 3D mesh pose transfer, the target model is obtained by transferring the pose of the reference mesh to the source mesh, where the shape and pose of the source are usually different from that of the reference. In this paper, pose transfer is considered as a deformation process of the source mesh, and we propose a 3D mesh pose transfer method based on skeletal deformation. First, we design a neural network based on the edge convolution operator to extract the skeleton of the 3D mesh and bind the rigid weights; then, we calculate the bone transformations between the two skeletons with different poses and use the diffusion equation to smooth the rigid weights; finally, the source mesh is deformed according to the bone transformations and the smooth weights to get the target mesh. Experiment results on different datasets show that the pose of the reference mesh can be effectively transferred to the source one while maintaining the shape and high‐quality geometric details of the source mesh by using our method.

Neutral Facial Rigging from Limited Spatiotemporal Meshes

“…For the generative joints and semantic parameters, we denote the loss function as L r , as in Equation (11), which contains six errors. The first is the recognized semantic error between the recognized semantic parameters and the labeled semantic parameters, provided in Equation (12).…”

“…In addition, blendshape-based rigging is slightly deficient in localized subtle deformation. The second alternative is skeleton-based rigging, which generates a specific pose by joint transformations [11] or nonlinear interpolation [12] in a pose space, then affects the vertex positions through weights. The pose space consists of joint positions, joint angles, and degrees of freedom at each level [12].…”