Wi-Fi signals-based passive gesture tracking is making quick progress in promoting human–computer interaction. The state-of-the-arts have achieved promising cm-level tracking accuracies via the Fresnel ellipses, while they depend heavily on the transceiver positions and the gesture initial positions. This problem is known as the position-dependence problem, which hinders the actual deployment of sensing applications. In this article, we propose EasyTrack, which aims at position-independency passive gesture tracking based on Wi-Fi channel state information (CSI). EasyTrack develops a novel incremental motion tracking model to correlate the length variations and the angle information of the reflection paths with the gesture traces. This model eliminates the dependence on the prior knowledge of transceiver positions and the gesture initial positions, by shifting the sensing observation from the traditional transceiver view to the antenna array-oriented view. We propose a series of tracking refinement approaches, including trace segmentation, tracking smoothing, and motion detection to improve the tracking accuracy. We prototype EasyTrack using off-the-shelf Wi-Fi radios and extensive experiments attest EasyTrack can achieve tracking accuracy of 0.9 cm under various users and environment conditions.