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Life sciences · Preprint
arXiv · September 4, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is an unpublished technical paper introducing SoundMHPE, the first reported method for multi-person 3D pose estimation from acoustic signals alone. The authors constructed a 6-hour, 432K-frame dataset (AMP) and report that their encoder-decoder framework outperforms baseline models, but the work lacks peer review, external validation, quantified performance metrics, and any clinical or practical application context.
Technical methods paper with custom dataset and proof-of-concept evaluation. Multiple people recorded in controlled acoustic setting; subject numbers, demographics, and activity types not specified.. Intervention: SoundMHPE: an encoder-decoder framework combining an Acoustic Multi-scale Encoder and Temporal Pose Decoder with attention mechanism to estimate multi-person 3D poses from acoustic signals.. Compared with: Baseline models (not named or detailed in the abstract).
SoundMHPE outperforms baseline models on the AMP dataset (no effect size or accuracy figures provided) AMP dataset comprises 432K synchronized frames of multi-person pose and acoustic data across 6 hours of recording First study to attempt multi-person 3D pose estimation from acoustic signals alone
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First-of-kind technical feasibility study presenting a novel method and custom dataset for an unexplored application; lacks external validation, clinical endpoints, or peer review.
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Quoted from the source exactly as published.
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Can we recover the 3D poses of multiple people using only sound? This paper presents the first attempt to estimate multi-person 3D poses solely from acoustic signals. Estimating the poses of multiple individuals using acoustic signals is inherently challenging due to the superposition of motion-dependent signal variations. Unlike single-person scenarios, the presence of multiple subjects leads to overlapping acoustic signatures, making it difficult to attribute specific signal changes to an individual's pose. Furthermore, the complexity is compounded by inter-person reflections, which introduce intricate propagation delays that obscure the temporal motion-acoustic relationship. To address these issues, we propose SoundMHPE (Sound-based Multi-person Human Pose Estimator), a novel encoder-decoder framework consisting of two key components. First, the Acoustic Multi-scale Encoder captures diverse temporal and fine-grained frequency features to isolate subtle acoustic signatures from complex, overlapping signals. Second, the Temporal Pose Decoder employs an attention mechanism to disentangle multi-person information across successive frames. By jointly accounting for temporal dynamics and inter-person dependencies, this component precisely reconstructs frame-wise individual poses. To validate our approach, we constructed the 6-hour Acoustic Multi-person Pose (AMP) dataset consisting of 432K synchronized frames of multi-person pose and acoustic data, and demonstrated that our SoundMHPE outperforms baseline models. Project page: https://oumi03.github.io/sound-mhpe/
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