This paper addresses the challenges of estimating a continuous-time field from a stream of events. Existing Human Mesh Recovery (HMR) methods rely predominantly on frame-based approaches, which are prone to aliasing and inaccuracies due to limited temporal resolution and motion blur. In this work, we predict a continuous-time human motion field from events caused by human motion. Prior state-of-the-art methods rely on computationally intensive optimization across a fixed number of poses at high frame rates, which becomes prohibitively expensive as we increase the temporal resolution. In comparison, our model leverages a recurrent feed-forward neural network to predict human motion in the latent space of possible human motions. We present the first work that replaces traditional event volume-based discrete-time pre-dictions with a continuous human motion field represented as a time-implicit function, enabling parallel pose queries at arbitrary temporal resolutions. To advance the evaluation of continuous-time human pose estimation, we introduce the Beam-splitter Event Agile Human Motion Dataset—a hardware-synchronized high-speed human dataset tailored for this purpose. EvHuman improves joint errors by 23.8 % compared to previous event human methods, while reducing the computational time by 69%.