  Although part-based motion synthesis networks have been investigated to
reduce the complexity of modeling heterogeneous human motions, their
computational cost remains prohibitive in interactive applications. To this
end, we propose a novel two-part transformer network that aims to achieve
high-quality, controllable motion synthesis results in real-time. Our network
separates the skeleton into the upper and lower body parts, reducing the
expensive cross-part fusion operations, and models the motions of each part
separately through two streams of auto-regressive modules formed by multi-head
attention layers. However, such a design might not sufficiently capture the
correlations between the parts. We thus intentionally let the two parts share
the features of the root joint and design a consistency loss to penalize the
difference in the estimated root features and motions by these two
auto-regressive modules, significantly improving the quality of synthesized
motions. After training on our motion dataset, our network can synthesize a
wide range of heterogeneous motions, like cartwheels and twists. Experimental
and user study results demonstrate that our network is superior to
state-of-the-art human motion synthesis networks in the quality of generated
motions.
