Temporal Rate Reduction Clustering for Human Motion Segmentation

Xianghan Meng; Zhengyu Tong; Zhiyuan Huang; Chun-Guang Li

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arXiv · 2506.21249

Temporal Rate Reduction Clustering for Human Motion Segmentation

Xianghan Meng·Zhengyu Tong·Zhiyuan Huang·Chun-Guang Li

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Abstract

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 ($\text{TR}^2\text{C}$), which jointly learns structured representations and affinity to segment the sequences of frames in video. Specifically, the structured representations learned by $\text{TR}^2\text{C}$ 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.

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Xianghan Meng, Zhengyu Tong, Zhiyuan Huang, Chun-Guang Li. 2025-06-26. Temporal Rate Reduction Clustering for Human Motion Segmentation. https://arxiv.org/abs/2506.21249

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