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Tipiano: Cascaded Piano Hand Motion Synthesis via Fingertip Priors

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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 fram

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    Computer Science > Artificial Intelligence [Submitted on 6 Apr 2026] Tipiano: Cascaded Piano Hand Motion Synthesis via Fingertip Priors Joonhyung Bae, Kirak Kim, Hyeyoon Cho, Sein Lee, Yoon-Seok Choi, Hyeon Hur, Gyubin Lee, Akira Maezawa, Satoshi Obata, Jonghwa Park, Jaebum Park, Juhan Nam 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: (1) statistics-based fingertip positioning, (2) FiLM-conditioned trajectory refinement, (3) wrist estimation, and (4) STGCN-based pose synthesis. We contribute expert-annotated fingerings for the FürElise dataset (153 pieces, ~10 hours). Experiments demonstrate F1 = 0.910, substantially outperforming diffusion baselines (F1 = 0.121), with user study (N=41) confirming quality approaching motion capture. Expert evaluation by professional pianists (N=5) identified anticipatory motion as the key remaining gap, providing concrete directions for future improvement. Subjects: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2604.09692 [cs.AI]   (or arXiv:2604.09692v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.09692 Focus to learn more Submission history From: Joonhyung Bae [view email] [v1] Mon, 6 Apr 2026 15:04:23 UTC (5,669 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.CV References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv AI
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    ◬ AI & Machine Learning
    Published
    Apr 14, 2026
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    Apr 14, 2026
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