用雷达数据精准估姿，还能知道自己错得多离谱。
RadProPoser: Probabilistic Radar Tensor Human Pose Estimation That Knows Its Limits
- 基于雷达张量的端到端概率建模，融合时序复数分量。
- 3D关节点误差仅6.425厘米，不确定性校准后更可靠。
- 适合隐私敏感场景，支持多雷达扩展，实时运行。
基于雷达的人体姿态估计可在不侵犯隐私的前提下实现环境智能中的运动追踪，但雷达传感的噪声特性使得不确定性量化至关重要。我们提出RadProPoser，一种端到端的概率框架，直接从原始雷达张量数据中预测三维人体关节位置及每个关节的不确定性。通过使用带谱注意力的变分编码器-解码器，融合时序帧中雷达的实部与虚部成分，以可学习的高斯和拉普拉斯分布建模随机不确定性（aleatoric uncertainty）。在包含光学动捕真值的新基准数据集上训练，该方法实现6.425厘米的平均关节点位置误差（MPJPE）。模型输出各关节点的随机不确定性，经等距校准后总不确定性达到预期校准误差0.027。由于谱注意力作用于独立雷达张量分量，扩展至多雷达配置仅需拼接额外输入流。在双正交雷达的HuPR基准上，该方法取得5.042厘米的MPJPE。该框架在NVIDIA RTX 3090上运行速度达89帧/秒，远超15赫兹的雷达帧率。
原文摘要 · Abstract (English)
Radar-based human pose estimation enables privacy-preserving motion tracking for ambient intelligence, yet the noisy nature of radar sensing makes uncertainty quantification essential. We present RadProPoser, an end-to-end probabilistic framework that predicts three-dimensional body joints with per-joint uncertainties from raw radar tensor data. Using a variational encoder-decoder with spectral attention that fuses real and imaginary radar components across temporal frames, we model aleatoric uncertainty through learnable Gaussian and Laplace distributions. Trained on a new benchmark dataset with optical motion-capture ground truth, our method achieves 6.425 cm mean per-joint position error. The model outputs per-joint aleatoric uncertainties, and isotonic recalibration yields calibrated total uncertainty with expected calibration error of 0.027. Since spectral attention operates on individual radar tensor components, extending to multi-radar configurations requires only concatenating additional input streams. On the HuPR benchmark with dual orthogonal radars, this achieves 5.042 cm MPJPE. The framework runs at 89 frames per second (FPS) on an NVIDIA RTX 3090, exceeding the 15 Hz radar frame rate.
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