aiqbench analysis
BGM2Pose represents a significant advancement in active sensing by leveraging natural audio signals to estimate 3D human poses without intrusive chirps. By utilizing contrastive learning and frequency-wise attention mechanisms, the system effectively isolates motion cues from complex musical backgrounds, addressing key challenges in non-invasive monitoring. This approach expands the practical utility of AI-driven acoustic sensing for real-world applications where user comfort is paramount.
BGM2Pose: Active 3D Human Pose Estimation with Non-Stationary Sounds
arXiv:2503.00389v2 Announce Type: replace-cross Abstract: We propose BGM2Pose, a non-invasive 3D human pose estimation method using arbitrary music (e.g., background music) as active sensing signals. Unlike existing approaches that significantly limit practicality by employing intrusive chirp signals within the audible range, our method utilizes natural music that causes minimal discomfort to humans. Estimating human poses from standard music presents significant challenges. In contrast to sound sources specifically designed for measurement, regular music varies in both volume and pitch. These dynamic changes in signals caused by music are inevitably mixed with alterations in the sound field resulting from human motion, making it hard to extract reliable cues for pose estimation. To address these challenges, BGM2Pose introduces a Contrastive Pose Extraction Module that employs contrastive learning and hard negative sampling to eliminate musical components from the recorded data, isolating the pose information. Additionally, we propose a Frequency-wise Attention Module that enables the model to focus on subtle acoustic variations attributable to human movement by dynamically computing attention across frequency bands. Experiments suggest that our method outperforms the existing methods, demonstrating substantial potential for real-world applications. Project page: https://yutoshibata07.github.io/bgm2pose-project-page/
Abstract:We propose BGM2Pose, a non-invasive 3D human pose estimation method using arbitrary music (e.g., background music) as active sensing signals. Unlike existing approaches that significantly limit practicality by employing intrusive chirp signals within the audible range, our method utilizes natural music that causes minimal discomfort to humans. Estimating human poses from standard music presents significant challenges. In contrast to sound sources specifically designed for measurement, regular music varies in both volume and pitch. These dynamic changes in signals caused by music are inevitably mixed with alterations in the sound field resulting from human motion, making it hard to extract reliable cues for pose estimation. To address these challenges, BGM2Pose introduces a Contrastive Pose Extraction Module that employs contrastive learning and hard negative sampling to eliminate musical components from the recorded data, isolating the pose information. Additionally, we propose a Frequency-wise Attention Module that enables the model to focus on subtle acoustic variations attributable to human movement by dynamically computing attention across frequency bands. Experiments suggest that our method outperforms the existing methods, demonstrating substantial potential for real-world applications. Project page: this https URL
Accepted by the IEEE Open Journal of Signal Processing, Vol. 7, 2026. Project page: this https URL
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Sound (cs.SD); Audio and Speech Processing (eess.AS)
arXiv:2503.00389 [cs.CV]
(or arXiv:2503.00389v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2503.00389 Focus to learn more arXiv-issued DOI via DataCite
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