Multi-View Representation Learning (MVRL) aims to discover a shared representation of observations from different views with the complex underlying correlation. In this paper, we propose a variational approach which casts MVRL as maximizing the amount of total correlation reduced by the representation, aiming to learn a shared latent representation that is informative yet succinct to capture the correlation among multiple views. To this end, we introduce a tractable surrogate objective function under the proposed framework, which allows our method to fuse and calibrate the observations in the representation space. From the information-theoretic perspective, we show that our framework subsumes existing multi-view generative models. Lastly, we show that our approach straightforwardly extends to the Partial MVRL (PMVRL) setting, where the observations are missing without any regular pattern. We demonstrate the effectiveness of our approach in the multi-view translation and classification tasks, outperforming strong baseline methods.