人类移动性数据对交通优化、城市规划和资源分配至关重要，但真实世界中的移动性数据因隐私问题而采集成本高昂且难以共享。近期基于扩散的方法在合成逼真移动模式方面展现出潜力，但通常依赖连续或潜在的时空轨迹，限制了其对具有显式区域、活动、时间和时间间隔结构的离散语义事件的原生建模能力。为解决此问题，我们提出 MobiDiff——一种端到端离散扩散框架，通过直接去噪多通道语义骨架高效生成移动性数据，避免了现有基于扩散方法中广泛采用的昂贵插值、潜在轨迹构建及由粗到细的实现流程。具体而言，MobiDiff 将每个人的签到事件分解为空间、活动和时间三个通道，并采用结构化的事件级、组级和通道级掩码策略，联合捕获轨迹级移动模式及事件内部依赖关系。我们在来自亚特兰大、波士顿和西雅图的三个大规模真实数据集上评估了生成保真度、隐私保护性与效率。结果表明，MobiDiff 有效保持了轨迹长度与时间间隔分布，同时在更广泛的移动性统计指标上保持竞争力；其推理速度亦显著优于当前最优方法，例如平均比 GeoGen 快 5.3 倍。这些发现表明，离散扩散为合成移动性数据提供了一种可解释且高效的框架。

Human mobility data are essential for transportation optimization, urban planning, and resource allocation, yet real-world mobility data are costly to collect and difficult to share due to privacy concerns. Recent diffusion-based methods have shown promise in synthesizing realistic mobility patterns, but they typically rely on continuous or latent spatio-temporal traces, limiting their ability to natively model discrete semantic events with explicit region, activity, time, and interval structures. To address this issue, we introduce MobiDiff, an end-to-end discrete diffusion framework that efficiently generates mobility data by directly denoising multi-channel semantic skeletons, avoiding the costly interpolation, latent trace construction, and coarse-to-fine realization pipelines widely used in existing diffusion-based methods. Specifically, MobiDiff decomposes each human check-in event into spatial, activity, and temporal channels, and employs structured event-, group-, and channel-level masking to jointly capture trajectory-level mobility patterns and within-event dependencies. We evaluate generation fidelity, privacy-preserving, and efficiency on three large-scale real-world datasets from Atlanta, Boston, and Seattle. Results show that MobiDiff effectively preserves trajectory length and temporal interval distributions while remaining competitive across broader mobility statistics; it is also much faster than state-of-the-art methods, e.g., 5.3$\times$ faster than GeoGen on average during inference. These findings suggest that discrete diffusion offers an interpretable and efficient framework for synthetic mobility data generation.