  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.
