Inverse tone mapping is an important topic in High Dynamic Range technology. Recent years, deep learning based image inverse tone mapping methods have been extensively studied and perform better than classical inverse tone mapping methods. However, these methods consider the inverse tone mapping problem as a domain transformation problem from LDR domain directly to HDR domain and ignore the relationship between LDR and HDR. Besides, when using these deep learning based methods to transform frames of videos, it will lead to temporal inconsistency and flickering. In this work, we propose a new way to consider the inverse tone mapping problem and design a deep learning based video inverse tone mapping algorithm to reduce the flickering. Different from previous methods, we first transform LDR resources back to approximate real scenes and use these real scenes to generate the HDR outputs. When generating HDR outputs, we use 3D convolutional neural network to reduce the flickering. We also use methods to further constrain the luminance information and the color information of HDR outputs separately. Finally, we compare our results with existing classical video inverse tone mapping algorithms and deep image inverse tone mapping methods to show our great performance, and we also prove the necessity of each part of our method.