The viewing safety is one of the main issues in viewing virtual reality (VR) content. In particular, VR sickness could occur when watching immersive VR content. To deal with the viewing safety for VR content, objective assessment of VR sickness is of great importance. In this paper, we propose a novel objective VR sickness assessment (VRSA) network based on deep generative model for automatically predicting the VR sickness score. The proposed method takes into account motion patterns of VR videos in which an exceptional motion is a critical factor inducing excessive VR sickness in human motion perception. The proposed VRSA network consists of two parts, which are VR video generator and VR sickness score predictor. By training the VR video generator with common videos with non-exceptional motion, the generator learns the tolerance of VR sickness in human motion perception. As a result, the difference between the original and the generated videos by the VR video generator could represent exceptional motion of VR video causing VR sickness. In the VR sickness score predictor, the VR sickness score is predicted by projecting the difference between the original and the generated videos onto the subjective score space. For the evaluation of VR sickness assessment, we built a new dataset which consists of 360° videos (stimuli), corresponding physiological signals, and subjective questionnaires from subjective assessment experiments. Experimental results demonstrated that the proposed VRSA network achieved a high correlation with human perceptual score for VR sickness.