Visible-Infrared Person Re-identification (VI-ReID) aims to retrieve person images across visible and infrared modalities, a task challenged by significant cross-modal discrepancies and intra-class variations. Existing methods primarily focus on matching images based on global or latent features, yet often neglect the prior information of human poses and the intrinsic identity-related cues guided by pose structures. To address this issue, we propose a Pose-Guided Cross-Modal Knowledge Distillation (PG-CMKD) framework, which enhances feature representation through pose-guided semantic alignment at both modality-specific and shared feature levels, thereby improving re-identification performance. Specifically, in order to compensate for the information loss caused by modal differences in cross modal data, we first extract modality-specific and shared features of persons. Then, we introduce learnable prototypes to extract body joints and progressively learn cross modal semantic invariance features of pose keypoints under their guidance, ensuring robust representations. These features are further combined with original modality features through local-part knowledge distillation to reinforce semantic consistency. Finally, global knowledge distillation is employed to explore latent inter-modal relationships within both specific and shared features, realize cross modal semantic alignment, thereby strengthening identity representations. Extensive experiments on multiple public datasets demonstrate the superior performance of our PG-CMKD framework, provides new insights into leveraging structural information for robust cross-modal person re-identification.