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 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 will provide our codebase and dataset for open access upon acceptance.

翻译：摘要：步入普适计算时代，人体运动感知在智能系统的决策制定、用户交互及个性化服务中扮演关键角色。现有研究主要集中于基于传统相机方法的人体追踪、姿态估计、手势识别与活动识别，但相机的侵入性限制了其在智能家居场景中的应用。为此，毫米波雷达凭借其隐私友好特性日益受到关注。本文提出milliFlow——一种用于毫米波点云场景流估计的新型深度学习方法，该场景流作为中层特征可为人机运动感知下游任务提供直接支撑。实验结果表明，本方法平均三维端点误差仅4.6厘米，性能显著优于现有方案。进一步地，通过引入场景流信息，我们在人体活动识别、人体解析及人体部位追踪任务中取得突破性提升。为促进该领域研究，论文接收后我们将开源代码库与数据集。