Problem
Research questions and friction points this paper is trying to address.
audio-driven synthesis
drummer motion
spatial-temporal precision
generalization
evaluation metrics
Generalized Audio-Driven Synthesis of Precise Drummer Motion
🤖 AI Summary
本文通过引入一种生成扩散框架和双目标损失函数，解决了从音频合成精确鼓手动作的问题，实现了高精度与自然身体动态的平衡。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
generative diffusion framework
dual-objective loss function
skeletal integrity
drumstick precision
data augmentation
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