Person re-identification (ReID) suffers from camera view variants. Existing works, which typically learn a feature for each image, share a limitation that the learned features are single-view: each feature only contains information in one camera view. Thus, view bias occurs when matching pedestrians across camera views. In this paper, we seek to mitigate the view bias by generating multi-view features (fusion of features from a fixed number of cameras). To this end, we define the complementary-view features (complementary features to generate multi-view features with single-view features) and perform in-depth analysis. Based on this insight, we alleviate the view bias in testing and training, respectively. In testing, we present Multi-view Message Passing (MVMP), which generates multi-view features by aggregating single-view features from the neighborhood. In training, we propose Multi-view Feature Fusion Network (MFFN), which involves the single-view feature extractor and the complementary-view feature aggregator. MFFN makes the network sensitive to view-specific cues by adding constraints on multi-view features rather than single-view features. In addition, MVMP and MFFN have two key advantages: (1) They are parameter-free. (2) They can be applied to any Convolutional Neural Networks (CNNs) readily without extra supervision. Extensive experiments are conducted to validate the superiority of our method for person ReID over state-of-the-art methods on four benchmark datasets (Market-1501, DukeMTMC-reID, CUHK03, and MSMT17). The code is available at https://github.com/Yinsongxu/MVMP_MFFN .