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arXiv · 2505.20920

HuMoCon: Concept Discovery for Human Motion Understanding

Qihang Fang·Chengcheng Tang·Bugra Tekin·Shugao Ma·Yanchao Yang
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Abstract

We present HuMoCon, a novel motion-video understanding framework designed for advanced human behavior analysis. The core of our method is a human motion concept discovery framework that efficiently trains multi-modal encoders to extract semantically meaningful and generalizable features. HuMoCon addresses key challenges in motion concept discovery for understanding and reasoning, including the lack of explicit multi-modality feature alignment and the loss of high-frequency information in masked autoencoding frameworks. Our approach integrates a feature alignment strategy that leverages video for contextual understanding and motion for fine-grained interaction modeling, further with a velocity reconstruction mechanism to enhance high-frequency feature expression and mitigate temporal over-smoothing. Comprehensive experiments on standard benchmarks demonstrate that HuMoCon enables effective motion concept discovery and significantly outperforms state-of-the-art methods in training large models for human motion understanding. We will open-source the associated code with our paper.

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Qihang Fang, Chengcheng Tang, Bugra Tekin, Shugao Ma, Yanchao Yang. 2025-05-27. HuMoCon: Concept Discovery for Human Motion Understanding. https://arxiv.org/abs/2505.20920

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