  Human motion sensing plays a crucial role in smart systems for
decision-making, user interaction, and personalized services. Extensive
research that has been conducted is predominantly based on cameras, whose
intrusive nature 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 approach to estimate
scene flow as 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 when compared with the competing approaches. Furthermore, by
incorporating scene flow information, we achieve remarkable improvements in
human activity recognition and human parsing and support human body part
tracking. Code and dataset are available at
https://github.com/Toytiny/milliFlow.
