Motion retargeting for animation characters has potential applications in fields such as animation production and virtual reality. However, current methods either assume that the source and target characters have the same skeletal structure, or require designing and training specific model architectures for each structure. In this article, we aim to address the challenge of motion retargeting across previously unseen skeletal structures with a unified dynamic graph network. The proposed approach utilizes a dynamic graph transformation module to dynamically transfer latent motion features to different structures. We also take into consideration for intricate hand movements and model both torso and hand joints as graphs in a unified manner for whole-body motion retargeting. Our model allows the use of motion data from different structures to train a unified model and learns cross-structural motion retargeting in an unsupervised manner with unpaired data. Experimental results demonstrate the superiority of the proposed method in terms of data efficiency and performance on both seen and unseen structures.

Capturing and preserving motion semantics is essential to motion retargeting between animation characters. However, most of the previous works neglect the semantic information or rely on human-designed joint-level representations. Here, we present a novel Semantics-aware Motion reTargeting (SMT) method with the advantage of vision-language models to extract and maintain meaningful motion semantics. We utilize a differentiable module to ren-der 3D motions. Then the high-level motion semantics are incorporated into the motion retargeting process by feeding the vision-language model with the rendered images and aligning the extracted semantic embeddings. To en-sure the preservation of fine-grained motion details and high-level semantics, we adopt a two-stage pipeline consisting of skeleton-aware pretraining and fine-tuning with semantics and geometry constraints. Experimental results show the effectiveness of the proposed method in producing high-quality motion retargeting results while accurately preserving motion semantics. Project page can be found at https://sites.google.com/view/smtnet.