Unsupervised Learning of Long-Term Motion Dynamics for Videos

@article{Luo2017UnsupervisedLO,
  title={Unsupervised Learning of Long-Term Motion Dynamics for Videos},
  author={Zelun Luo and Boya Peng and De-An Huang and Alexandre Alahi and Li Fei-Fei},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017},
  pages={7101-7110}
}
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… CONTINUE READING
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