In this paper, a novel deep generative model-based approach for video error concealment is proposed. Our method is comprised of completion network and two critics. The frame completion network is trained to fool the both the local and global critics, which requires completion network to conceal frame distortions with regard to overall consistency as well as in details. Specifically, mask attention convolution layer is proposed, which utilize not only the temporal information of the previous frame, but also the intact pixels of the current distorted frame to mask and re-normalize convolution features. Then, both qualitative and quantitative experiments validate the effectiveness and generality of our approach in advancing the error concealment on low resolution video.