  Synthesizing controllable motion for a character using deep learning has been
a promising approach due to its potential to learn a compact model without
laborious feature engineering. To produce dynamic motion from weak control
signals such as desired paths, existing methods often require auxiliary
information such as phases for alleviating motion ambiguity, which limits their
generalisation capability. As past poses often contain useful auxiliary hints,
in this paper, we propose a task-agnostic deep learning method, namely
Multi-scale Control Signal-aware Transformer (MCS-T), with an attention based
encoder-decoder architecture to discover the auxiliary information implicitly
for synthesizing controllable motion without explicitly requiring auxiliary
information such as phase. Specifically, an encoder is devised to adaptively
formulate the motion patterns of a character's past poses with multi-scale
skeletons, and a decoder driven by control signals to further synthesize and
predict the character's state by paying context-specialised attention to the
encoded past motion patterns. As a result, it helps alleviate the issues of low
responsiveness and slow transition which often happen in conventional methods
not using auxiliary information. Both qualitative and quantitative experimental
results on an existing biped locomotion dataset, which involves diverse types
of motion transitions, demonstrate the effectiveness of our method. In
particular, MCS-T is able to successfully generate motions comparable to those
generated by the methods using auxiliary information.
