Millimeter wave (mmWave) sensing promises to enable contactless and high-precision “in air" gesture-based human-computer interaction (HCI). While previous works have demonstrated its feasibility, they require tedious gesture collecting for person-independent recognition and they operate in an off-line mode without considering practical issues like segmenting gesture and recognition latency. In this work, we propose , a person-independent real-time mmWave gesture recognition solution. We first build a compact gesture model with a custom-designed neural network to distill the unique features underlying each gesture, while suppressing personalized discrepancy across different users without extra collection and re-training. Furthermore, we design a system status transition to decide when a gesture begins and ends, which enables automatic gesture segmentation and hence real-time recognition. We prototype on a commodity mmWave sensor and demonstrate its advantages using two practical applications: a contactless music player and camera. Extensive experiments and user studies show that has an accuracy of 99% and a short response latency within 25ms. Moreover, we also collect and release a comprehensive mmWave gesture dataset consisting of 54,620 instances from 144 persons, which may have an independent value of facilitating future research.