为毫米波人体姿态估计添加关节可靠性描述，提升下游任务准确率。
mmJoints: Expanding Joint Representations Beyond (x,y,z) in mmWave-Based 3D Pose Estimation
- 在已有姿态估计器输出中增加关节感知概率与定位可靠性描述。
- 关节描述误差低于4.2%，位置精度提升12.5%，动作识别准确率提高16%。
- 适合关注毫米波感知可信度与可解释性的研究人员。
在基于毫米波的姿态估计中，信号稀疏和弱反射常导致模型依赖统计先验而非传感器数据推断关节位置。虽然先验知识有助于学习有意义的表示，但过度依赖会降低手势与活动识别等下游任务性能。本文提出mmJoints框架，通过增强预训练黑箱毫米波3D姿态估计器的输出，添加额外的关节描述符。该方法不消除偏差，而是显式估计每个关节的感知可能性及其位置预测的可靠性。这些描述符提升了模型可解释性，并改善了下游任务精度。在覆盖13种姿态估计设置、超过11.5万帧信号数据的实验中，mmJoints的描述符估计误差低于4.2%；关节位置精度最高提升12.5%，活动识别准确率相较现有方法最高提升16%。
原文摘要 · Abstract (English)
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.
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