Unsupervised Learning of Long-Term Motion Dynamics for Videos

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… (More)
DOI: 10.1109/CVPR.2017.751

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