Unsupervised Representation Learning With Long-Term Dynamics for Skeleton Based Action Recognition

In recent years, skeleton based action recognition is becoming an increasingly attractive alternative to existing videobased approaches, beneficial from its robust and comprehensive 3D information. In this paper, we explore an unsupervised representation learning approach for the first time to capture the long-term global motion dynamics in skeleton sequences. We design a conditional skeleton inpainting architecture for learning a fixed-dimensional representation, guided by additional adversarial training strategies. We quantitatively evaluate the effectiveness of our learning approach on three well-established action recognition datasets. Experimental results show that our learned representation is discriminative for classifying actions and can substantially reduce the sequence inpainting errors.

Authors

• Nenggan Zheng Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, Zhejiang, China College of Computer Science and Techology, Zhejiang University, Hangzhou, Zhejiang, China State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, Zhejiang, China CCAI by MOE and Zhejiang Provincial Government(ZJU), Hangzhou, Zhejiang, China
• Jun Wen
• Risheng Liu Dalian University of Technology
• Liangqu Long
• Jianhua Dai
• Zhefeng Gong

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