  Although skeleton-based action recognition has achieved great success in
recent years, most of the existing methods may suffer from a large model size
and slow execution speed. To alleviate this issue, we analyze skeleton sequence
properties to propose a Double-feature Double-motion Network (DD-Net) for
skeleton-based action recognition. By using a lightweight network structure
(i.e., 0.15 million parameters), DD-Net can reach a super fast speed, as 3,500
FPS on one GPU, or, 2,000 FPS on one CPU. By employing robust features, DD-Net
achieves the state-of-the-art performance on our experimental datasets: SHREC
(i.e., hand actions) and JHMDB (i.e., body actions). Our code will be released
with this paper later.
