  Generating long-range skeleton-based human actions has been a challenging
problem since small deviations of one frame can cause a malformed action
sequence. Most existing methods borrow ideas from video generation, which
naively treat skeleton nodes/joints as pixels of images without considering the
rich inter-frame and intra-frame structure information, leading to potential
distorted actions. Graph convolutional networks (GCNs) is a promising way to
leverage structure information to learn structure representations. However,
directly adopting GCNs to tackle such continuous action sequences both in
spatial and temporal spaces is challenging as the action graph could be huge.
To overcome this issue, we propose a variant of GCNs to leverage the powerful
self-attention mechanism to adaptively sparsify a complete action graph in the
temporal space. Our method could dynamically attend to important past frames
and construct a sparse graph to apply in the GCN framework, well-capturing the
structure information in action sequences. Extensive experimental results
demonstrate the superiority of our method on two standard human action datasets
compared with existing methods.
