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.

中文翻译：

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时间引导的音乐到身体运动生成

本文提出了一种神经网络模型，可从音乐音频中生成虚拟小提琴手的 3D 骨架运动。所提出的模型从先前工作中用于生成二维骨架数据的传统循环神经网络模型进行了改进，该模型结合了编码器-解码器架构，以及自我注意机制来模拟身体运动序列中的复杂动态。为了促进自注意力模型的优化，应用节拍跟踪来确定训练示例的有效大小和边界。解码器配有精炼网络和弓箭攻击推理机制，以强调右手行为和弓箭攻击时机。客观和主观评估都表明，所提出的模型优于最先进的方法。据我们所知，