NeMo-map: Neural Implicit Flow Fields for Spatio-Temporal Motion Mapping

ICLR 2026 Conference Submission21314 Authors

Keywords: Neural Implicit Representation, Human Motion Representation, Maps of Dynamics

TL;DR: A continuous spatio-temporal map of dynamics using implicit neural representations, achieving more accurate and efficient motion pattern representation and faster training.

Abstract: 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 a large public dataset with long-term 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.

Primary Area: applications to robotics, autonomy, planning

Submission Number: 21314