Metraipolis › archive › dc_ea709388ce50

Generalized Audio-Driven Synthesis of Precise Drummer Motion — arXiv:2608.19055

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.19055
License (as archived): CC0-1.0
Archived: 2026-10-18T14:06:33.957Z
SHA-256 of the archived text: e18f67979eca723951de31de9b931b5bc0928c8038c883fbbea0ee3011314865
Stable URL of this page: https://metraipolis.com/documento/dc_ea709388ce50

How to cite

Generalized Audio-Driven Synthesis of Precise Drummer Motion — arXiv:2608.19055 — arXiv (en), https://arxiv.org/abs/2608.19055 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ea709388ce50 (Metraipolis archive dc_ea709388ce50, sha256 e18f67979eca723951de31de9b931b5bc0928c8038c883fbbea0ee3011314865)

BibTeX

@misc{dcea709388ce50,
  title = {Generalized Audio-Driven Synthesis of Precise Drummer Motion — arXiv:2608.19055},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.19055},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ea709388ce50; sha256 e18f67979eca723951de31de9b931b5bc0928c8038c883fbbea0ee3011314865},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.19055. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Music-driven character animation enables and enhances transformative applications in entertainment and interactive education. However, synthesizing realistic drumming motion from audio remains challenging due to the inherent tension between high-acceleration dynamics and the need for extreme spatial-temporal precision. Existing approaches, often reliant on motion matching or MIDI input, struggle with generalizing to diverse real-world audio. Moreover, the field lacks standardized evaluation metrics capable of distinguishing precise drumming from noisy motion. In this paper, we introduce a generative diffusion framework featuring a dual-objective loss function that decouples skeletal integrity from drumstick precision, thus enabling centimeter-level stick precision without sacrificing natural body dynamics. Additionally, leveraging our own dataset and data augmentation strategy, the model generalizes to non-curated, in-the-wild audio. To rigorously evaluate performance, we propose two novel metrics: an impact-to-target distance to quantify spatial precision and an audio-motion correlation score to assess temporal alignment. Our quantitative analysis and user studies demonstrate that our system generates high-quality motion that is often indistinguishable from ground-truth performances.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-20 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Graphics (cs.GR); Sound (cs.SD).
Pagina del lavoro: https://arxiv.org/abs/2608.19055 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T14:00:22.594Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-08-20?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).