This work studies the challenge of transfer animations between characters
whose skeletal topologies differ substantially. While many techniques have
advanced retargeting techniques in decades, transfer motions across diverse
topologies remains less-explored. The primary obstacle lies in the inherent
topological inconsistency between source and target skeletons, which restricts
the establishment of straightforward one-to-one bone correspondences. Besides,
the current lack of large-scale paired motion datasets spanning different
topological structures severely constrains the development of data-driven
approaches. To address these limitations, we introduce Motion2Motion, a novel,
training-free framework. Simply yet effectively, Motion2Motion works with only
one or a few example motions on the target skeleton, by accessing a sparse set
of bone correspondences between the source and target skeletons. Through
comprehensive qualitative and quantitative evaluations, we demonstrate that
Motion2Motion achieves efficient and reliable performance in both
similar-skeleton and cross-species skeleton transfer scenarios. The practical
utility of our approach is further evidenced by its successful integration in
downstream applications and user interfaces, highlighting its potential for
industrial applications. Code and data are available at
https://lhchen.top/Motion2Motion.