Researchers in the field of Image Processing and Computer Vision have been working hard to understand and resolve the problems arising due to depth of field (DOF) effects and relative motion between the scene and imaging system. The varying degradation seen in the image of an object due to its depth characteristics is called depth dependent defocus blur and the blurring effect observed in the images of moving objects is called motion blur. We have developed a set of algorithms to generate shift-variant defocus and motion blur effects and have presented the same through this thesis. Our algorithm precisely takes into account the rigid body motion of 3-D objects which includes translational and rotational motion in any arbitrary direction. Camera parameters such as aperture diameter, focal length and the location of image detector are used to calculate the blur circle radius of Point spread functions (PSFs) modeled by Gaussian and Cylindrical functions. We also describe a novel and simple method of image inpainting used for filling the missing pixels that arise due to round off errors occurred during interpolation or changes in magnification. Experiments have been carried out on a set of 3-D shapes like, but not limited to, sphere, cylinder, cone, etc. Results are documented to demonstrate the correctness of our algorithm. The methods described through this thesis can be used in generating realistic 3D animation. The results are also useful as simulated test data with known ground truth in the testing and evaluation of image and video de-blurring algorithms.