Abstract
We present a simple yet effective method for skeleton-agnostic motion retargeting. Previous methods transfer motion between high-resolution meshes, failing to preserve the inherent local-part motions in the mesh. Addressing this issue, our proposed method learns the correspondence in a coarse-to-fine fashion by disentangling the retargeting process within multi-scale meshes. First, we propose a mesh-pooling module that pools the mesh representations for better motion transfer. This module improves the ability to handle small-part motion and preserves the local motion interdependence between neighboring mesh vertices. Furthermore, we leverage a multi-scale refinement procedure to complement missing mesh details by gradually refining the low-resolution mesh output with a higher-resolution one. We evaluate our method on several well-known 3D character datasets, and it yields an average improvement of 25% on point-wise mesh Euclidean distance (PMD) against the start-of-art method. Qualitative results show that our method is significantly helpful in preserving the moving consistency of different body parts on the target character due to disentangling body-part structures and mesh details in a multi-scale way.