  Motion retargeting is the long-standing problem in character animation that
consists in transferring and adapting the motion of a source character to
another target character. A typical application is the creation of motion
sequences from off-the-shelf motions by transferring them onto new characters.
Motion retargeting is also promising to increase interoperability of existing
animation systems and motion databases, as they often differ in the structure
of the skeleton(s) considered. Moreover, since the goal of motion retargeting
is to abstract and transfer motion dynamics, effective solutions might provide
expressive and powerful human motion models in which operations such as
cleaning or editing are easier. In this article, we present a novel neural
network architecture for retargeting that extracts an abstract representation
of human motion agnostic to skeleton topology and morphology. Based on
transformers, our model is able to encode and decode motion sequences with
variable morphology and topology -- extending the current scope of retargeting
-- while supporting skeleton topologies not seen during the training phase.
More specifically, our model is structured as an autoencoder, and encoding and
decoding are separately conditioned on skeleton templates to extract and
control morphology and topology. Beyond motion retargeting, our model has many
applications since our abstract representation is a convenient space to embed
motion data from different sources. It may potentially be benefical to a number
of data-driven methods, allowing them to combine scarce specialised motion
datasets (e.g. with style or contact annotations) and larger general motion
datasets, for improved performance and generalisation ability. Moreover, we
show that our model can be useful for other applications beyond retargeting,
including motion denoising and joint upsampling.
