Marker-less motion capture is a challenging problem, particularly when only monocular video is available. We estimate human motion from monocular video by recovering three-dimensional controllers capable of implicitly simulating the observed human behavior and replaying this behavior in other environments and under physical perturbations. Our approach employs a state-space biped controller with a balance feedback mechanism that encodes control as a sequence of simple control tasks. Transitions among these tasks are triggered on time and on proprioceptive events (e.g., contact). Inference takes the form of optimal control where we optimize a high-dimensional vector of control parameters and the structure of the controller based on an objective function that compares the resulting simulated motion with input observations. We illustrate our approach by automatically estimating controllers for a variety of motions directly from monocular video. We show that the estimation of controller structure through incremental optimization and refinement leads to controllers that are more stable and that better approximate the reference motion. We demonstrate our approach by capturing sequences of walking, jumping, and gymnastics.