In the past few years, a large range of wireless signals such as WiFi, RFID, UWB and Millimeter Wave were utilized for sensing purposes. Among these wireless sensing modalities, WiFi sensing attracts a lot of attention owing to the pervasiveness of WiFi infrastructure in our surrounding environments. While WiFi sensing has achieved a great success in capturing the target's motion information ranging from coarse-grained activities and gestures to fine-grained vital signs, it still has difficulties in precisely obtaining the target size owing to the low frequency and small bandwidth of WiFi signals. Even Millimeter Wave radar can only achieve a very coarse-grained size measurement. High precision object size sensing requires using RF signals in the extremely high-frequency band (e.g., Terahertz band). In this paper, we utilize low-frequency WiFi signals to achieve accurate object size measurement without requiring any learning or training. The key insight is that when an object moves between a pair of WiFi transceivers, the WiFi CSI variations contain singular points (i.e., singularities) and we observe an exciting opportunity of employing the number of singularities to measure the object size. In this work, we model the relationship between the object size and the number of singularities when an object moves near the LoS path, which lays the theoretical foundation for the proposed system to work. By addressing multiple challenges, for the first time, we make WiFi-based object size measurement work on commodity WiFi cards and achieve a surprisingly low median error of 2.6 mm. We believe this work is an important missing piece of WiFi sensing and opens the door to size measurement using low-cost low-frequency RF signals.