Acoustic-based 3D Human Pose Estimation Robust to Human Position

TL;DR

This paper introduces a novel acoustic-based 3D human pose estimation method that remains accurate regardless of human position, using adversarial learning and reverberation-resistant modeling to improve robustness in real-world scenarios.

Contribution

It proposes a position discriminator with adversarial learning and a reverberation-resistant model to enhance pose estimation accuracy across diverse human locations.

Findings

Outperforms existing methods in diverse locations

Robust to variations in sound arrival times

Effective in real-world scenarios

Abstract

This paper explores the problem of 3D human pose estimation from only low-level acoustic signals. The existing active acoustic sensing-based approach for 3D human pose estimation implicitly assumes that the target user is positioned along a line between loudspeakers and a microphone. Because reflection and diffraction of sound by the human body cause subtle acoustic signal changes compared to sound obstruction, the existing model degrades its accuracy significantly when subjects deviate from this line, limiting its practicality in real-world scenarios. To overcome this limitation, we propose a novel method composed of a position discriminator and reverberation-resistant model. The former predicts the standing positions of subjects and applies adversarial learning to extract subject position-invariant features. The latter utilizes acoustic signals before the estimation target time as references to enhance robustness against the variations in sound arrival times due to diffraction and reflection. We construct an acoustic pose estimation dataset that covers diverse human locations and demonstrate through experiments that our proposed method outperforms existing approaches.