1,321 research outputs found
Unsupervised Learning of Long-Term Motion Dynamics for Videos
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.Comment: CVPR 201
Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation
For human pose estimation in monocular images, joint occlusions and
overlapping upon human bodies often result in deviated pose predictions. Under
these circumstances, biologically implausible pose predictions may be produced.
In contrast, human vision is able to predict poses by exploiting geometric
constraints of joint inter-connectivity. To address the problem by
incorporating priors about the structure of human bodies, we propose a novel
structure-aware convolutional network to implicitly take such priors into
account during training of the deep network. Explicit learning of such
constraints is typically challenging. Instead, we design discriminators to
distinguish the real poses from the fake ones (such as biologically implausible
ones). If the pose generator (G) generates results that the discriminator fails
to distinguish from real ones, the network successfully learns the priors.Comment: Fixed typos. 14 pages. Demonstration videos are
http://v.qq.com/x/page/c039862eira.html,
http://v.qq.com/x/page/f0398zcvkl5.html,
http://v.qq.com/x/page/w0398ei9m1r.htm
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