To eliminate motion-induced errors, a multi-step fitting position transformation method is proposed for measuring the three-dimensional (3D) profile of an object. The method employs a neural network to perform position transformation on the objects in the captured motion phase-shifting fringe projection images, aligning them to the same position, and then uses the phase-shifting method to obtain the 3D profile of the objects. Theoretically, the rigid body motion transformation matrix is used to deduce the change of points on the objects in the fringe pattern under different motion conditions, and the 3D profile measurement results of randomly moving objects are simulated and analyzed. Experimentally, a neural network model was trained by using 5000 sets of data obtained from experiments to eliminate motion-induced errors in various motion states. Simulation and experimental results show that this method significantly reduces the motion-induced errors that occur when measuring moving objects using the traditional phase-shifting method. It has broad application prospects in the field of real-time measurement of moving objects’ 3D profiles.