  This paper presents a neural network model to generate virtual violinist's
3-D skeleton movements from music audio. Improved from the conventional
recurrent neural network models for generating 2-D skeleton data in previous
works, the proposed model incorporates an encoder-decoder architecture, as well
as the self-attention mechanism to model the complicated dynamics in body
movement sequences. To facilitate the optimization of self-attention model,
beat tracking is applied to determine effective sizes and boundaries of the
training examples. The decoder is accompanied with a refining network and a
bowing attack inference mechanism to emphasize the right-hand behavior and
bowing attack timing. Both objective and subjective evaluations reveal that the
proposed model outperforms the state-of-the-art methods. To the best of our
knowledge, this work represents the first attempt to generate 3-D violinists'
body movements considering key features in musical body movement.
