mmJoints：在基于mmWave的3D姿态估计中扩展超越(x,y,z)的关节表示
mmJoints: Expanding Joint Representations Beyond (x,y,z) in mmWave-Based 3D Pose Estimation
AI总结：
提出mmJoints框架，通过附加关节被感知可能性及预测位置可靠性的描述符，显性化基于mmWave的3D姿态估计中的先验偏差，从而提升下游任务的准确率与可解释性。
摘要：
在基于mmWave的姿态估计中，稀疏信号和微弱反射通常导致模型依赖统计先验而非传感器数据来推断身体关节。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和活动识别等下游任务的性能。在本文中，我们提出了mmJoints框架，通过附加关节描述符来增强预训练的、黑盒式基于mmWave的3D姿态估计器的输出。mmJoints并非缓解偏差，而是通过估计关节被感知的可能性及其预测位置的可靠性来使偏差显性化。这些描述符增强了可解释性并提升了下游任务的准确性。通过在13种姿态估计设置下对超过115,000个信号帧进行广泛评估，我们表明mmJoints估计描述符的错误率低于4.2%。与最先进的方法相比，mmJoints还将关节位置准确率最高提升了12.5%，并将活动识别率最高提升了16%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
