-
[pdf]
[supp]
[arXiv]
[bibtex]@InProceedings{Fang_2025_CVPR, author = {Fang, Qihang and Tang, Chengcheng and Tekin, Bugra and Ma, Shugao and Yang, Yanchao}, title = {HuMoCon: Concept Discovery for Human Motion Understanding}, booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)}, month = {June}, year = {2025}, pages = {7179-7190} }
HuMoCon: Concept Discovery for Human Motion Understanding
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.
Related Material