人类活动轨迹（HATs）对诸多应用至关重要，包括人类移动性建模与兴趣点（POI）推荐。然而，日益增长的隐私关切严重限制了真实大规模HAT数据集的获取。生成式人工智能的最新进展为此类应用提供了合成高保真且隐私保护型HAT的新机遇。但仍有两大挑战亟待解决：（i）HAT具有高度不规则性与动态性，时间间隔长且变化显著，难以准确刻画其复杂的时空依赖关系及底层分布；（ii）生成模型通常计算开销大，导致长期、细粒度HAT合成效率低下。为应对上述挑战，我们提出SynHAT——一种基于新型时空去噪扩散模型的计算高效、由粗到细HAT合成框架。第一阶段，我们构建Coarse-HADiff，用于建模粗粒度潜在时空轨迹的整体时空依赖关系；该模块引入一种新颖的潜在时空U-Net结构，并配备双分支Drift-Jitter机制，以在去噪过程中联合建模平滑的空间转移与时间变化。第二阶段，我们设计三步流程：行为模式提取、Fine-HADiff（架构与Coarse-HADiff一致）以及语义对齐，以从第一阶段输出中生成细粒度潜在时空轨迹。我们从数据保真度、实用性、隐私性、鲁棒性与可扩展性五个维度对SynHAT进行了全面评估。在来自三个国家四座城市的现实世界HAT数据集上的实验表明，SynHAT显著优于现有最先进基线方法，在空间与时间指标上分别提升52%与33%。

Human activity traces (HATs) are critical for many applications, including human mobility modeling and point-of-interest (POI) recommendation. However, growing privacy concerns have severely limited access to authentic large-scale HAT datasets. Recent advances in generative AI provide new opportunities to synthesize realistic and privacy-preserving HATs for such applications. Yet two major challenges remain: (i) HATs are highly irregular and dynamic, with long and varying time intervals, making it difficult to capture their complex spatio-temporal dependencies and underlying distributions; and (ii) generative models are often computationally expensive, making long-term, fine-grained HAT synthesis inefficient. To address these challenges, we propose SynHAT, a computationally efficient coarse-to-fine HAT synthesis framework built on a novel spatio-temporal denoising diffusion model. In Stage 1, we develop Coarse-HADiff, which models the overall spatio-temporal dependencies of coarse-grained latent spatio-temporal traces. It incorporates a novel Latent Spatio-Temporal U-Net with dual Drift-Jitter branches to jointly model smooth spatial transitions and temporal variations during denoising. In Stage 2, we introduce a three-step pipeline consisting of Behavior Pattern Extraction, Fine-HADiff, which shares the same architecture as Coarse-HADiff, and Semantic Alignment to generate fine-grained latent spatio-temporal traces from the Stage 1 outputs. We extensively evaluate SynHAT in terms of data fidelity, utility, privacy, robustness, and scalability. Experiments on real-world HAT datasets from four cities across three countries show that SynHAT substantially outperforms state-of-the-art baselines, achieving 52% and 33% improvements on spatial and temporal metrics, respectively.