Zelun Luo

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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)
We propose a viewpoint invariant model for 3D human pose estimation from a single depth image. To achieve this, our discrimina-tive model embeds local regions into a learned viewpoint invariant feature space. Formulated as a multi-task learning problem, our model is able to selectively predict partial poses in the presence of noise and occlusion. Our(More)
Hand hygiene has been shown to be an effective intervention to reduce transmission and infections in many studies. This project focuses on interpreting visual clinical data for hand hygiene monitoring. We propose two distinct deep learning approaches to detect hand hygiene action on manually collected and labeled data. Specifically, we investigate a(More)
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