Abstract
In mmWave-based pose estimation, sparse signals and weak reflections often cause models to infer body joints from statistical priors rather than sensor data. While prior knowledge helps in learning meaningful representations, over-reliance on it degrades performance in downstream tasks like gesture and activity recognition. In this paper, we introduce mmJoints, a framework that augments a pre-trained, black-box mmWave-based 3D pose estimator's output with additional joint descriptors. Rather than mitigating bias, mmJoints makes it explicit by estimating the likelihood of a joint being sensed and the reliability of its predicted location. These descriptors enhance interpretability and improve downstream task accuracy. Through extensive evaluations using over 115,000 signal frames across 13 pose estimation settings, we show that mmJoints estimates descriptors with an error rate below 4.2%. mmJoints also improves joint position accuracy by up to 12.5% and boosts activity recognition by up to 16% over state-of-the-art methods.
Abstract (translated)
在基于毫米波（mmWave）的姿态估计中，稀疏信号和弱反射经常导致模型从统计先验而非传感器数据来推断人体关节位置。虽然先验知识有助于学习有意义的表示，但过度依赖它会降低手势识别和活动识别等下游任务的表现。本文介绍了 mmJoints 框架，该框架通过添加额外的关节描述符来增强预训练、黑盒 mmWave 基于 3D 姿势估计器的输出。mmJoints 不是减轻偏见，而是使其明确化，通过对一个关节被感知的概率和其预测位置的可靠性进行评估来体现这一点。这些描述符增强了模型的可解释性，并提高了下游任务的准确性。 通过使用超过115,000个信号帧，在13种姿态估计设置下进行了广泛的实验评估，结果表明 mmJoints 估算的描述符错误率低于4.2%。此外，mmJoints 还将关节位置精度提高了最多12.5%，并将活动识别性能提升了高达16%，优于最先进的方法。
URL
https://arxiv.org/abs/2510.08970
