In this simulation study, we present and examine methods to develop a feedback controller for a neuroprosthesis that restores forward and side leaning function during standing following complete thoracic-level spinal cord injury. Achieving leaning postures away from erect stance with functional neuromuscular stimulation (FNS) would allow users to extend their reaching capabilities. Utilizing a 3-D computer model of human stance, an FNS control system based on total-body center of mass (CoM) kinematics (position, acceleration) is developed and tested in simulation. CoM kinematics drive an artificial neural network to modulate muscle excitations and reduce the upper extremity loading, presumably against a walker or similar support surface, required to resist the effects of postural perturbations. Furthermore, a novel method to robustly estimate the feedback kinematics for standing applications is also presented while assuming 3-D accelerometer signals at locations consistent with a proposed implantable networked neuroprosthesis system. For shifting and balance at leaning postures, respectively, center of mass position and acceleration could be approximated to within 20% of the maximum value, with strong correlations (R > 0.9) between values estimated by the proposed method and the true values derived from model dynamics. When utilizing the estimated feedback kinematics for FNS control, standing performance in terms of maximum upper extremity loading was still significantly reduced (p < 0.001) compared to conventionally applying constant and maximal stimulation. In the future, these simulation-based methods will be employed to develop experimental approaches for restoring leaning standing function by FNS.