人类移动性生成旨在合成真实的兴趣点（POI）访问轨迹，已成为出行行为建模、交通管理和城市规划的重要工具。现有的基于扩散模型的方法虽然保真度高，但由于地理空间位置和 POI 类别固有的异质性，需要针对每个城市单独生成；而基于大语言模型的方法虽能实现跨城市泛化，但在大规模场景下成本过高，尤其对于长时域轨迹生成而言。为解决这一问题，我们提出了 SeMoFlow，一种基于潜在流匹配（latent Flow matching）的语义人类移动性生成框架。首先，我们通过分层语义 ID（Semantic IDs）将不同城市的异构 POI 编码到共享的跨城市表示空间中，其中共享前缀捕捉可迁移的语义，后续代码逐步细化表示以指向具体 POI。基于语义 ID，SeMoFlow 遵循“规划-合成”的分层生成范式：自回归规划器生成粗粒度的语义和重复模式，流匹配实现器合成细粒度的后缀潜变量。生成的潜变量随后被解码并映射到具体的 POI。在大规模多城市数据集上的广泛实验表明，SeMoFlow 相比现有基线方法实现了更高的轨迹保真度，保留了特定城市的移动性特征，并支持联合多城市生成及有效的跨城市迁移。

Human mobility generation aims to synthesize realistic point-of-interest (POI) visitation trajectories and has become an important tool for travel behavior modeling, transportation management, and urban planning. Existing diffusion-based methods achieve high fidelity but require per-city generation, given the inherent heterogeneity of geospatial locations and POI categories, while large language model-based methods generalize across cities but remain too costly at scale, especially for long-horizon trajectory generation. To address this, we propose SeMoFlow, a Semantic human Mobility generation framework based on latent Flow matching. We first encode heterogeneous POIs from different cities into a shared cross-city representation space via hierarchical Semantic IDs, where shared prefixes capture transferable semantics, and successive codes progressively refine the representation toward individual POIs. Building on the semantic IDs, SeMoFlow follows a plan-to-synthesis hierarchical generation paradigm, in which an autoregressive planner generates coarse-grained semantic and recurrence patterns, and a flow matching realizer synthesizes fine-grained suffix latents. The generated latents are subsequently decoded and grounded to concrete POIs. Extensive experiments on large-scale multi-city datasets show that SeMoFlow achieves higher trajectory fidelity than existing baselines, preserves city-specific mobility motifs, and supports both joint multi-city generation and effective cross-city transfer.