# Efficient Unsupervised Temporal Segmentation of Motion Data

arXiv: https://arxiv.org/abs/1510.06595 · HTML: https://mlsanity.com/paper/1510.06595/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2015.

## Built on by (most cited first, 7 of 7)

- [MotionBERT: A Unified Perspective on Learning Human Motion Representations](https://mlsanity.com/paper/2210.06551/) (2022)
- [Temporal Action Segmentation: An Analysis of Modern Techniques](https://mlsanity.com/paper/2210.10352/) (2022)
- [Learning Transferable Motor Skills with Hierarchical Latent Mixture Policies](https://mlsanity.com/paper/2112.05062/) (2021)
- [AthletePose3D: A Benchmark Dataset for 3D Human Pose Estimation and Kinematic Validation in Athletic Movements](https://mlsanity.com/paper/2503.07499/) (2025)
- [SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration](https://mlsanity.com/paper/2211.13743/) (2022)
- [Skeleton Motion Words for Unsupervised Skeleton-Based Temporal Action Segmentation](https://mlsanity.com/paper/2508.04513/) (2025)
- [Dynamical Deep Generative Latent Modeling of 3D Skeletal Motion](https://mlsanity.com/paper/2106.10393/) (2021)

## Abstract (arXiv)

We introduce a method for automated temporal segmentation of human motion data into distinct actions and compositing motion primitives based on self-similar structures in the motion sequence. We use neighbourhood graphs for the partitioning and the similarity information in the graph is further exploited to cluster the motion primitives into larger entities of semantic significance. The method requires no assumptions about the motion sequences at hand and no user interaction is required for the segmentation or clustering. In addition, we introduce a feature bundling preprocessing technique to make the segmentation more robust to noise, as well as a notion of motion symmetry for more refined primitive detection. We test our method on several sensor modalities, including markered and markerless motion capture as well as on electromyograph and accelerometer recordings. The results highlight our system's capabilities for both segmentation and for analysis of the finer structures of motion data, all in a completely unsupervised manner.