# Temporal Rate Reduction Clustering for Human Motion Segmentation

arXiv: https://arxiv.org/abs/2506.21249 · HTML: https://mlsanity.com/paper/2506.21249/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 70 references that are in the map.
- First posted on arXiv 26 Jun 2025.

## Builds on (most cited first, 28 of 28)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction](https://mlsanity.com/paper/2006.08558/) (2020)
- [Unsupervised Learning from Narrated Instruction Videos](https://mlsanity.com/paper/1506.09215/) (2015)
- [Temporal Action Segmentation: An Analysis of Modern Techniques](https://mlsanity.com/paper/2210.10352/) (2022)
- [Unsupervised learning of action classes with continuous temporal embedding](https://mlsanity.com/paper/1904.04189/) (2019)
- [Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation](https://mlsanity.com/paper/2103.11264/) (2021)
- [Robust and Efficient Subspace Segmentation via Least Squares Regression](https://mlsanity.com/paper/1404.6736/) (2014)
- [Neural Manifold Clustering and Embedding](https://mlsanity.com/paper/2201.10000/) (2022)
- [Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering](https://mlsanity.com/paper/1605.02633/) (2016)
- [A Perceptual Prediction Framework for Self Supervised Event Segmentation](https://mlsanity.com/paper/1811.04869/) (2018)
- [Temporally Consistent Unbalanced Optimal Transport for Unsupervised Action Segmentation](https://mlsanity.com/paper/2404.01518/) (2024)
- [Self-Supervised Learning via Maximum Entropy Coding](https://mlsanity.com/paper/2210.11464/) (2022)
- [Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework](https://mlsanity.com/paper/1610.05211/) (2016)
- [Learning a Self-Expressive Network for Subspace Clustering](https://mlsanity.com/paper/2110.04318/) (2021)
- [Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models](https://mlsanity.com/paper/2306.05272/) (2023)
- [Hierarchical Vector Quantization for Unsupervised Action Segmentation](https://mlsanity.com/paper/2412.17640/) (2024)
- [A Critique of Self-Expressive Deep Subspace Clustering](https://mlsanity.com/paper/2010.03697/) (2020)
- [A Global Geometric Analysis of Maximal Coding Rate Reduction](https://mlsanity.com/paper/2406.01909/) (2024)
- [Unsupervised Manifold Linearizing and Clustering](https://mlsanity.com/paper/2301.01805/) (2023)
- [CoSeg: Cognitively Inspired Unsupervised Generic Event Segmentation](https://mlsanity.com/paper/2109.15170/) (2021)
- [Consistency and Diversity induced Human Motion Segmentation](https://mlsanity.com/paper/2202.04861/) (2022)
- [Exploring a Principled Framework for Deep Subspace Clustering](https://mlsanity.com/paper/2503.17288/) (2025)
- [Graph Constrained Data Representation Learning for Human Motion Segmentation](https://mlsanity.com/paper/2107.13362/) (2021)
- [Enhancing temporal segmentation by nonlocal self-similarity](https://mlsanity.com/paper/1906.11335/) (2019)

## Built on by (most cited first, 1 of 1)

- [Jointly Learning Structured Representations and Stabilized Affinity for Human Motion Segmentation](https://mlsanity.com/paper/2605.05753/) (2026)

## Abstract (arXiv)

Human Motion Segmentation (HMS), which aims to partition videos into non-overlapping human motions, has attracted increasing research attention recently. Existing approaches for HMS are mainly dominated by subspace clustering methods, which are grounded on the assumption that high-dimensional temporal data align with a Union-of-Subspaces (UoS) distribution. However, the frames in video capturing complex human motions with cluttered backgrounds may not align well with the UoS distribution. In this paper, we propose a novel approach for HMS, named Temporal Rate Reduction Clustering (TR2C), which jointly learns structured representations and affinity to segment the sequences of frames in video. Specifically, the structured representations learned by TR2C enjoy temporally consistency and are aligned well with a UoS structure, which is favorable for addressing the HMS task. We conduct extensive experiments on five benchmark HMS datasets and achieve state-of-the-art performances with different feature extractors. The code is available at: https://github.com/mengxianghan123/TR2C.