  A good motion retargeting cannot be reached without reasonable consideration
of source-target differences on both the skeleton and shape geometry levels. In
this work, we propose a novel Residual RETargeting network (R2ET) structure,
which relies on two neural modification modules, to adjust the source motions
to fit the target skeletons and shapes progressively. In particular, a
skeleton-aware module is introduced to preserve the source motion semantics. A
shape-aware module is designed to perceive the geometries of target characters
to reduce interpenetration and contact-missing. Driven by our explored
distance-based losses that explicitly model the motion semantics and geometry,
these two modules can learn residual motion modifications on the source motion
to generate plausible retargeted motion in a single inference without
post-processing. To balance these two modifications, we further present a
balancing gate to conduct linear interpolation between them. Extensive
experiments on the public dataset Mixamo demonstrate that our R2ET achieves the
state-of-the-art performance, and provides a good balance between the
preservation of motion semantics as well as the attenuation of interpenetration
and contact-missing. Code is available at https://github.com/Kebii/R2ET.
