Person re-identification (ReID) aims to retrieve pedestrians across cameras, facing challenges from differences in perspective, background, and lighting, which introduce noise and hinder key feature extraction. Existing methods, often relying on normalization or generative data augmentation, suffer from limitations such as neglecting camera label information or the unreliability of two-stage learning. To address this, we propose a one-stage architecture, M-MBNNet, consisting of MBN (Multi Background Norm) and MetaRep (Meta-Representation for Adaptive Metric) modules. MBN uses a camera-wise Assignment Gate and Multi-aggregation Norm to align and normalize backgrounds, reducing interference and enhancing person-relevant feature robustness. MetaRep bridges representation and metric learning, leveraging mutual information (quality measures) to dynamically adjust asymmetric metrics for consistent multi-task convergence. It also incorporates curriculum learning to dynamically emphasize either inter-class separability or intra-class compactness. M-MBNNet offers a systematic approach to extracting key pedestrian features and resolving cross-camera differences through active alignment and adaptive optimization. We achieve strong results on two baselines—one mainly for representation and one for metric learning—demonstrating the method's scalability.