With the emergence of the Microsoft Kinect sensor, many developer communities and research groups have found countless uses and have already published a wide variety of papers that utilize the raw depth images for their specific goals. New methods and applications that use the device generally require an appropriately large ensemble of data sets with accompanying ground truth for testing purposes, as well as accurate models that account for the various systematic and stochastic contributors to Kinect errors. Current error models, however, overlook the intermediate infrared (IR) images that directly contribute to noisy depth estimates. We, therefore, propose a high fidelity Kinect IR and depth image predictor and simulator that models the physics of the transmitter/receiver system, unique IR dot pattern, disparity/depth processing technology, and random intensity speckle and IR noise in the detectors. The model accounts for important characteristics of Kinect's stereo triangulation system, including depth shadowing, IR dot splitting, spreading, and occlusions, correlation-based disparity estimation between windows of measured and reference IR images, and subpixel refinement. Results show that the simulator accurately produces axial depth error from imaged flat surfaces with various tilt angles, as well as the bias and standard lateral error of an object's horizontal and vertical edge.