Recent studies have demonstrated the effectiveness of warping in transferring unique textures to the output of the pose transfer networks. However, due to the mutual dependencies of image features and pixel locations, joint estimation of flow map and output image is very likely to get stuck in local minima. Current solution is limited to offline estimation of the maps. However, in this way the flow is generated without interaction withthe incarnation parts of the generative model, causing it to struggle with the occlusion parts of samples. To address the issue, we introduce a patch generation module which acts as a mediator between the output values and flow estimations, cutting their mutual dependencies while encouraging the flow maps to merely focus on regions that are not correctly generated by the patch estimations, regions like clothing with unique colors ortextures that due to the scarcity of data can not be properly learned during the training phase of the network. Our patch generation module benefits from two individual experts on removing the visible parts of the source sample which disappear in the target view and drawing those invisible parts which appear in the novel view of the sample. Experimental results demonstrate that our method outperforms the state-of-the-art on two well-knowndatabases, Deepfashion and Market1501.