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

Unsupervised Learning of Long-Term Motion Dynamics for Videos