Sounding Bodies: Modeling 3D Spatial Sound of Humans Using Body Pose and Audio

Xudong Xu, Dejan Markovic, Jacob Sandakly, Todd Keebler, Steven Krenn +1 more
11/1/2023

No official code found. Best implementation: facebookresearch/SoundingBodies (93 stars, last commit 2024-05-29).

Abstract

While 3D human body modeling has received much attention in computer vision, modeling the acoustic equivalent, i.e. modeling 3D spatial audio produced by body motion and speech, has fallen short in the community. To close this gap, we present a model that can generate accurate 3D spatial audio for full human bodies. The system consumes, as input, audio signals from headset microphones and body pose, and produces, as output, a 3D sound field surrounding the transmitter's body, from which spatial audio can be rendered at any arbitrary position in the 3D space. We collect a first-of-its-kind multimodal dataset of human bodies, recorded with multiple cameras and a spherical array of 345 microphones. In an empirical evaluation, we demonstrate that our model can produce accurate body-induced sound fields when trained with a suitable loss. Dataset and code are available online.

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We present a model that can generate accurate 3D sound fields of human bodies from headset microphones and body pose as inputs.

934PythonOct 13, 20232 years agoNOASSERTION

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@article{xu2023sounding,
  title  = {Sounding Bodies: Modeling 3D Spatial Sound of Humans Using Body Pose and Audio},
  author = {Xudong Xu and Dejan Markovic and Jacob Sandakly and Todd Keebler and Steven Krenn and Alexander Richard},
  year   = {2023},
  eprint = {2311.06285},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2311.06285},
  journal = {NEURIPS 2023 2023}
}

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