  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 render 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 ensure the preservation of
fine-grained motion details and high-level semantics, we adopt a two-stage
pipeline consisting of skeleton-aware pre-training 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.
