Unsupervised Learning of Long-Term Motion Dynamics for Videos

Zelun Luo; Boya Peng; De-An Huang; Alexandre Alahi; Li Fei-Fei

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arXiv · 1701.01821

Unsupervised Learning of Long-Term Motion Dynamics for Videos

Zelun Luo·Boya Peng·De-An Huang·Alexandre Alahi·Li Fei-Fei

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Abstract

We present an unsupervised representation learning approach that compactly encodes the motion dependencies in videos. Given a pair of images from a video clip, our framework learns to predict the long-term 3D motions. To reduce the complexity of the learning framework, we propose to describe the motion as a sequence of atomic 3D flows computed with RGB-D modality. We use a Recurrent Neural Network based Encoder-Decoder framework to predict these sequences of flows. We argue that in order for the decoder to reconstruct these sequences, the encoder must learn a robust video representation that captures long-term motion dependencies and spatial-temporal relations. We demonstrate the effectiveness of our learned temporal representations on activity classification across multiple modalities and datasets such as NTU RGB+D and MSR Daily Activity 3D. Our framework is generic to any input modality, i.e., RGB, Depth, and RGB-D videos.

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BibTeX RIS

Zelun Luo, Boya Peng, De-An Huang, Alexandre Alahi, Li Fei-Fei. 2017-04-11. Unsupervised Learning of Long-Term Motion Dynamics for Videos. https://arxiv.org/abs/1701.01821

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