Ding, F; Luo, Z; Zhao, P; Lu, CX; (2025) milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing. In: Leonardis, A and Ricci, E and Roth, S and Russakovsky, O and Sattler, T and Varol, G, (eds.) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). (pp. pp. 202-221). Springer Nature: Cham, Switzerland.

Text 2306.17010v8.pdf - Accepted Version Access restricted to UCL open access staff until 4 November 2025. Download (9MB)

Abstract

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.

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