The multi-view data with incomplete information hinder effective data analysis. Existing multi-view imputation methods, which learn the mapping between a complete view and acompletely missingview, are not able to deal with the typical multi-view data withmissing featureinformation. In this paper, we propose a unified generative imputation model named UGit with optimal transport theory to simultaneously impute the missing features/values of all incomplete views. This imputation is conditional onallthe observed values from the multi-view data. UGit consists of two modules, i.e., aunified multi-view generator(UMG) and amasking energy discriminator(MED). To effectively and efficiently impute missing features across all views, the generator UMG employs aunified autoencoderin conjunction with thecross-view attention mechanismto learn the data distribution from all observed multi-view data. The discriminator MED leverages a novelmasking energydivergence function to make UGit differentiable for imputation accuracy enhancement. Extensive experiments on several real-world multi-view data sets demonstrate that, UGit speeds up the model training by 4.28x with more than 41% accuracy gain on average, compared to the state-of-the-art approaches.