{"slug": "generalized-audio-driven-synthesis-of-precise-drummer-motion", "title": "Generalized Audio-Driven Synthesis of Precise Drummer Motion", "summary": "Researchers from Tel-Aviv University, DisneyResearch|Studios, and ETH Zurich introduced a generative diffusion framework with a dual-objective loss function that decouples skeletal integrity from drumstick precision, enabling centimeter-level stick precision without sacrificing natural body dynamics. The model, trained on a new dataset with data augmentation, generalizes to in-the-wild audio and outperforms existing methods on two novel metrics: impact-to-target distance for spatial precision and audio-motion correlation for temporal alignment. User studies show the generated motion is often indistinguishable from ground-truth performances.", "body_md": "# Generalized Audio-Driven Synthesis of Precise Drummer Motion\n\n### In this work, 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.\n\n**August 18, 2026**\n\n**arXiv (2026)**\n\n#### Authors\n\nÁlvaro G. Iñesta (The Blavatnik School of Computer Science and AI, Tel-Aviv University)\n\nMattia Ryffel (DisneyResearch|Studios)\n\nAmit H. Bermano (DisneyResearch|Studios/The Blavatnik School of Computer Science and AI, Tel-Aviv University/ETH Zurich)\n\nRobert W. Sumner (DisneyResearch|Studios/ETH Zurich)\n\nMartin Guay (DisneyResearch|Studios)\n\n#### Generalized Audio-Driven Synthesis of Precise Drummer Motion\n\nMusic-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.", "url": "https://wpnews.pro/news/generalized-audio-driven-synthesis-of-precise-drummer-motion", "canonical_source": "https://studios.disneyresearch.com/2026/08/18/generalized-audio-driven-synthesis-of-precise-drummer-motion/", "published_at": "2026-08-19 03:10:13+00:00", "updated_at": "2026-08-19 03:41:00.391823+00:00", "lang": "en", "topics": ["artificial-intelligence", "generative-ai", "machine-learning"], "entities": ["Tel-Aviv University", "DisneyResearch|Studios", "ETH Zurich", "Álvaro G. Iñesta", "Mattia Ryffel", "Amit H. Bermano", "Robert W. Sumner", "Martin Guay"], "alternates": {"html": "https://wpnews.pro/news/generalized-audio-driven-synthesis-of-precise-drummer-motion", "markdown": "https://wpnews.pro/news/generalized-audio-driven-synthesis-of-precise-drummer-motion.md", "text": "https://wpnews.pro/news/generalized-audio-driven-synthesis-of-precise-drummer-motion.txt", "jsonld": "https://wpnews.pro/news/generalized-audio-driven-synthesis-of-precise-drummer-motion.jsonld"}}