Despite extensive research effort on contactless WiFi sensing over the past few years, there are still significant barriers hindering its wide application. One key issue is the limited sensing range due to the intrinsic nature of employing the weak target-reflected signal for sensing and therefore the sensing range is much smaller than the communication range. In this work, we address this challenging issue, moving WiFi sensing one step closer to real-world adoption. The key idea is to effectively utilize the multiple antennas widely available on commodity WiFi access points to simultaneously strengthen the target-reflected signal and reduce the noise. Although traditional beamforming schemes can help increase the signal strength, they are designed for communication and can not be directly applied to benefit sensing. To effectively increase the WiFi sensing range using multiple antennas, we first propose a new metric that quantifies the signal sensing capability. We then propose novel signal processing methods, which lay the theoretical foundation to support beamforming-based long-range WiFi sensing. To validate the proposed idea, we develop two sensing applications: fine-grained human respiration monitoring and coarse-grained human walking tracking. Extensive experiments show that: (i) the human respiration sensing range is significantly increased from the state-of-the-art 6-8 m to 11 m; 1 and (ii) human walking can be accurately tracked even when the target is 18 m away from the WiFi transceivers, outperforming the sensing range of the state-of-the-art by 50%. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing systems and tools;