  Motion retargeting is a fundamental problem in computer graphics and computer
vision. Existing approaches usually have many strict requirements, such as the
source-target skeletons needing to have the same number of joints or share the
same topology. To tackle this problem, we note that skeletons with different
structure may have some common body parts despite the differences in joint
numbers. Following this observation, we propose a novel, flexible motion
retargeting framework. The key idea of our method is to regard the body part as
the basic retargeting unit rather than directly retargeting the whole body
motion. To enhance the spatial modeling capability of the motion encoder, we
introduce a pose-aware attention network (PAN) in the motion encoding phase.
The PAN is pose-aware since it can dynamically predict the joint weights within
each body part based on the input pose, and then construct a shared latent
space for each body part by feature pooling. Extensive experiments show that
our approach can generate better motion retargeting results both qualitatively
and quantitatively than state-of-the-art methods. Moreover, we also show that
our framework can generate reasonable results even for a more challenging
retargeting scenario, like retargeting between bipedal and quadrupedal
skeletons because of the body part retargeting strategy and PAN. Our code is
publicly available.
