Tipiano: Cascaded Piano Hand Motion Synthesis via Fingertip Priors
Summary: arXiv:2604.09692v1 Announce Type: new
Abstract
Synthesizing realistic piano hand motions requires both precision and naturalness. Physics-based methods achieve precision but produce stiff motions; data-driven models learn natural dynamics but struggle with positional accuracy. Piano motion exhibits a natural hierarchy: fingertip positions are nearly deterministic given piano geometry and fingering, while wrist and intermediate joints offer stylistic freedom. We present [OURS], a four-stage framework exploiting this hierarchy:
- Statistics-based fingertip positioning: Utilizes geometric constraints to accurately position fingertips.
- FiLM-conditioned trajectory refinement: Enhances the trajectory of finger movements using conditioning methods.
- Wrist estimation: Calculates the movement of the wrist to complement fingertip motions.
- STGCN-based pose synthesis: Employs spatio-temporal graph convolutional networks to synthesize realistic poses.
Contributions
We contribute expert-annotated fingerings for the F\”urElise dataset, which includes 153 pieces totaling approximately 10 hours of piano music. This dataset serves as a benchmark for evaluating the quality of synthesized hand motions.
Experimental Results
Our experiments demonstrate a remarkable F1 score of 0.910, substantially outperforming diffusion baselines with an F1 score of only 0.121. This indicates a significant improvement in the accuracy and realism of the synthesized motions. A user study involving 41 participants confirmed that the quality of our synthesized motions approaches that of motion capture technology.
Expert Evaluation
An expert evaluation was conducted with five professional pianists. They identified anticipatory motion as the key remaining gap in our model, providing concrete directions for future improvements. The feedback from these experts is invaluable for refining the synthesis process and enhancing the overall quality of the generated hand motions.
Future Directions
Based on the insights gained from expert evaluations, future work will focus on addressing the anticipatory motion challenge. This may involve integrating more sophisticated motion prediction techniques and enhancing the model’s ability to generate stylistically varied performances. By continuing to explore the relationship between finger, wrist, and overall hand motion, we aim to further bridge the gap between synthesized and authentic piano performances.
Conclusion
The proposed Tipiano framework presents a significant advancement in the field of piano hand motion synthesis. By leveraging a hierarchical approach to finger positioning and incorporating innovative techniques for trajectory refinement and pose synthesis, we are paving the way for more realistic and expressive piano performances in virtual environments.