# The Magni Human Motion Dataset: Accurate, Complex, Multi-Modal, Natural, Semantically-Rich and Contextualized

arXiv: https://arxiv.org/abs/2208.14925 · HTML: https://mlsanity.com/paper/2208.14925/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2022.

## Built on by (most cited first, 9 of 9)

- [A Landmark-Aware Visual Navigation Dataset](https://mlsanity.com/paper/2402.14281/) (2024)
- [Online Context Learning for Socially Compliant Navigation](https://mlsanity.com/paper/2406.11495/) (2024)
- [Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review](https://mlsanity.com/paper/2506.14831/) (2025)
- [TH"OR-MAGNI Act: Actions for Human Motion Modeling in Robot-Shared Industrial Spaces](https://mlsanity.com/paper/2412.13729/) (2024)
- [Causality-enhanced Decision-Making for Autonomous Mobile Robots in Dynamic Environments](https://mlsanity.com/paper/2504.11901/) (2025)
- [Collecting Human Motion Data in Large and Occlusion-Prone Environments using Ultra-Wideband Localization](https://mlsanity.com/paper/2505.05851/) (2025)
- [Trajectory prediction for heterogeneous agents: A performance analysis on small and imbalanced datasets](https://mlsanity.com/paper/2510.03776/) (2025)
- [Gaze4HRI: Zero-shot Benchmarking Gaze Estimation Neural-Networks for Human-Robot Interaction](https://mlsanity.com/paper/2605.04770/) (2026)
- [ACME: A Multi-Cultural, Multi-Embodiment Social-Navigation Dataset](https://mlsanity.com/paper/2607.21964/) (2026)

## Abstract (arXiv)

Rapid development of social robots stimulates active research in human motion modeling, interpretation and prediction, proactive collision avoidance, human-robot interaction and co-habitation in shared spaces. Modern approaches to this end require high quality datasets for training and evaluation. However, the majority of available datasets suffers from either inaccurate tracking data or unnatural, scripted behavior of the tracked people. This paper attempts to fill this gap by providing high quality tracking information from motion capture, eye-gaze trackers and on-board robot sensors in a semantically-rich environment. To induce natural behavior of the recorded participants, we utilise loosely scripted task assignment, which induces the participants navigate through the dynamic laboratory environment in a natural and purposeful way. The motion dataset, presented in this paper, sets a high quality standard, as the realistic and accurate data is enhanced with semantic information, enabling development of new algorithms which rely not only on the tracking information but also on contextual cues of the moving agents, static and dynamic environment.