<p>We present a novel event-based representation, named Motion-Encoded Time-Surface (METS), and how it can be used to address the challenge of pose tracking under high-speed scenarios with an event camera. The core concept is dynamically encoding the pixel-wise decay rate of the Time-Surface to account for localized spatio-temporal scene dynamics captured by events, rendering remarkable adaptability with respect to motion dynamics. The consistency between METS and the scene in highly dynamic conditions establishes a reliable foundation for robust pose estimation. Building upon this, we employ a semi-dense 3D-2D alignment pipeline to fully unlock the potential of the event camera for high-speed tracking applications. Given the intrinsic characteristics of METS, we further develop specialized lightweight operations aimed at minimizing the per-event computational cost. The proposed algorithm is successfully evaluated on public datasets and our high-speed motion datasets covering various scenes and motion complexities. It shows that our approach outperforms state-of-the-art pose tracking methods, especially in highly dynamic scenarios, and is capable of tracking accurately under incredibly fast motions that are inaccessible for other event- or frame-based counterparts. Due to its simplicity, our algorithm exhibits outstanding practicality, running at over 70 Hz on a standard CPU.</p>