Deep neural networks for video based eye tracking have demonstrated resilience to noisy environments, stray reflections and low resolution. However, to train these networks, a large number of manually annotated images are required. To alleviate the cumbersome process of manual labeling, computer graphics rendering is employed to automatically generate a large corpus of annotated eye images under various conditions. In this work, we introduce RIT-Eyes, a novel synthetic eye image generation platform which improves upon previous work by adding features such as retinal retro-reflection, realistic blinks, an active deformable iris and an aspherical cornea. We add various external influences which potentially degrade eye tracking such as corrective eye-wear with varying refractive indices. To demonstrate the utility of RIT-Eyes, we generate and publicly share a large dataset of images with a variety of eye poses and viewing conditions.