用毫米波雷达实现黑暗中人体动作预测，克服光照与隐私问题。
mmPred: Radar-based Human Motion Prediction in the Dark
- 首创基于扩散模型的雷达动作预测框架，融合时域与频域信息。
- 在mmBody和mm-Fi数据集上分别提升8.6%和22%性能。
- 适合消防、医疗等暗光或隐私敏感场景使用。
基于RGB-D相机的人体动作预测方法对光照敏感且存在隐私问题，限制了其在消防、医疗等场景的应用。本文首次将毫米波雷达引入人体动作预测，利用其抗干扰与隐私保护优势。针对雷达信号易受镜面反射和多径效应影响导致的测量噪声与关节漏检问题，提出mmPred——首个专为雷达设计的扩散模型框架。该方法采用双域历史运动表示：时域姿态精修分支（TPR）捕捉细节，频域主导运动分支（FDM）建模全局趋势并抑制帧级不一致。同时设计全局骨架关系变压器（GST）作为扩散主干，通过关节间动态信息聚合，增强受损关节的恢复能力。大量实验表明，mmPred在mmBody和mm-Fi数据集上分别领先现有方法8.6%和22%。
原文摘要 · Abstract (English)
Existing Human Motion Prediction (HMP) methods based on RGB-D cameras are sensitive to lighting conditions and raise privacy concerns, limiting their real-world applications such as firefighting and healthcare. Motivated by the robustness and privacy-preserving nature of millimeter-wave (mmWave) radar, this work introduces radar as a novel sensing modality for HMP, for the first time. Nevertheless, radar signals often suffer from specular reflections and multipath effects, resulting in noisy and temporally inconsistent measurements, such as body-part miss-detection. To address these radar-specific artifacts, we propose mmPred, the first diffusion-based framework tailored for radar-based HMP. mmPred introduces a dual-domain historical motion representation to guide the generation process, combining a Time-domain Pose Refinement (TPR) branch for learning fine-grained details and a Frequency-domain Dominant Motion (FDM) branch for capturing global motion trends and suppressing frame-level inconsistency. Furthermore, we design a Global Skeleton-relational Transformer (GST) as the diffusion backbone to model global inter-joint cooperation, enabling corrupted joints to dynamically aggregate information from others. Extensive experiments show that mmPred achieves state-of-the-art performance, outperforming existing methods by 8.6% on mmBody and 22% on mm-Fi.
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