Approaching the era of ubiquitous computing, human motion sensing plays a crucial role in smart systems for decision making, user interaction, and personalized services. Extensive research has been conducted on human tracking, pose estimation, gesture recognition, and activity recognition, which are predominantly based on cameras in traditional methods. However, the intrusive nature of cameras limits their use in smart home applications. To address this, mmWave radars have gained popularity due to their privacy-friendly features. In this work, we propose \textit{milliFlow}, a novel deep learning method for scene flow estimation as a complementary motion information for mmWave point cloud, serving as an intermediate level of features and directly benefiting downstream human motion sensing tasks. Experimental results demonstrate the superior performance of our method with an average 3D endpoint error of 4.6cm, significantly surpassing the competing approaches. Furthermore, by incorporating scene flow information, we achieve remarkable improvements in human activity recognition, human parsing, and human body part tracking. To foster further research in this area, we provide our codebase and dataset for open access.

翻译：随着普适计算时代的到来，人体运动感知在智能系统的决策制定、用户交互与个性化服务中扮演着关键角色。大量研究聚焦于人体跟踪、姿态估计、手势识别及活动识别等任务，传统方法主要基于摄像头实现。然而，摄像头的侵入性特征限制了其在智能家居场景中的应用。为解决这一问题，毫米波雷达因其隐私友好特性而获得广泛关注。本文提出一种名为\textit{milliFlow}的新型深度学习方法，用于毫米波点云场景流估计，以此作为互补性运动信息，在特征中间层直接服务于下游人体运动感知任务。实验结果表明，本方法性能优越，平均三维端点误差仅为4.6厘米，显著超越现有竞争方法。此外，通过融合场景流信息，我们在人体活动识别、人体解析及人体部位跟踪任务中均取得显著提升。为促进该领域的进一步研究，我们公开提供代码库与数据集。