Recently, artificial intelligence techniques for education have been received increasing attentions, while it still remains an open problem to design the effective music instrument instructing systems. Although key presses can be directly derived from sheet music, the transitional movements among key presses require more extensive guidance in piano performance. In this work, we construct a piano-hand motion generation benchmark to guide hand movements and fingerings for piano playing. To this end, we collect an annotated dataset, PianoMotion10M, consisting of 116 hours of piano playing videos from a bird's-eye view with 10 million annotated hand poses. We also introduce a powerful baseline model that generates hand motions from piano audios through a position predictor and a position-guided gesture generator. Furthermore, a series of evaluation metrics are designed to assess the performance of the baseline model, including motion similarity, smoothness, positional accuracy of left and right hands, and overall fidelity of movement distribution. Despite that piano key presses with respect to music scores or audios are already accessible, PianoMotion10M aims to provide guidance on piano fingering for instruction purposes. The dataset and source code can be accessed at https://agnjason.github.io/PianoMotion-page.

通过构建一个钢琴手运动生成基准和采集一个包含10百万个手部姿势注释的钢琴演奏视频数据集PianoMotion10M，该研究提出了一个钢琴指法的指导系统，通过音频生成手部运动，并评估了其性能。

PianoMotion10M：钢琴演奏中手部动作生成的数据集与基准