Safe and efficient robot operation in complex human environments can benefit from good models of site-specific motion patterns. Maps of Dynamics (MoDs) provide such models by encoding statistical motion patterns in a map, but existing representations use discrete spatial sampling and typically require costly offline construction. We propose a continuous spatio-temporal MoD representation based on implicit neural functions that directly map coordinates to the parameters of a Semi-Wrapped Gaussian Mixture Model. This removes the need for discretization and imputation for unevenly sampled regions, enabling smooth generalization across both space and time. Evaluated on two public datasets with real-world people tracking data, our method achieves better accuracy of motion representation and smoother velocity distributions in sparse regions while still being computationally efficient, compared to available baselines. The proposed approach demonstrates a powerful and efficient way of modeling complex human motion patterns and high performance in the trajectory prediction downstream task. Project code is available at https://github.com/test-bai-cpu/nemo-map

翻译：在复杂人类环境中实现安全高效的机器人运行，可以从良好的特定场景运动模式建模中获益。动态地图通过在地图中编码统计运动模式来提供此类模型，但现有表示方法采用离散空间采样，通常需要昂贵的离线构建。我们提出了一种基于隐式神经函数的连续时空动态地图表示方法，该方法直接将坐标映射到半包裹高斯混合模型的参数。这消除了对离散化和非均匀采样区域插补的需求，实现了跨空间和时间的平滑泛化。在两个包含真实世界行人跟踪数据的公共数据集上的评估表明，与现有基线方法相比，我们的方法在运动表示精度和稀疏区域速度分布平滑性方面表现更优，同时仍保持计算效率。所提出的方法展示了建模复杂人类运动模式的高效强大能力，并在轨迹预测下游任务中表现出优异性能。项目代码发布于 https://github.com/test-bai-cpu/nemo-map