Unsupervised Learning of Long-Term Motion Dynamics for Videos

CVPR, 2017.

Cited by: 105|Bibtex|Views109
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

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 com...More

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