MOMO: Human Motion from Moving Sounds

Abstract:Human responses to sound depend not only on its semantic content, but also on the evolving spatial relationship between source and listener, conveyed through spatial audio and its temporal dynamics. Modeling human reactions to such dynamic spatial audio is important for character animation, human behavior analysis, and robotics. As of yet, existing audio-driven motion generation methods primarily focus on non-spatial or static acoustic conditions, leaving this important problem largely underexplored. To bridge this gap, we formulate the novel task of dynamic spatial-audio-driven human motion generation and introduce two motion-captured datasets. We introduce the Dynamic Spatial Audio and Motion dataset (DySAM) as the first dataset and benchmark for this task, providing synchronized binaural audio, 3D human motions, and moving sound source trajectories. Given the importance of dynamic spatial cues of sound sources, we further provide the Dynamic Spatial Audio Localization dataset (DySAL), an auxiliary binaural localization dataset that pairs diverse acoustic events with head poses and moving source trajectories to support spatial audio perception. For benchmarking, we propose MOMO for human Motion generation from Moving Sounds, a diffusion-based framework guided by three coupled principles: localization, understanding, and generation. MOMO performs localization by estimating head-centric spatial direction cues of the moving source directly from raw binaural audio, avoiding reliance on ground-truth source coordinates. It performs understanding by extracting frame-wise audio-content representations, and performs generation by conditioning motion diffusion on both semantic features and dynamic spatial cues. Experiments show that MOMO outperforms existing baselines in motion fidelity, audio-motion alignment, and physical plausibility.

Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Robotics (cs.RO); Sound (cs.SD)

arXiv:2610.24522 [cs.CV]

(or arXiv:2610.24522v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2610.24522 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.