Learning Latent Representations of 3D Human Pose with Deep Neural Networks

Published: 31/01/2018
Learning Latent Representations of 3D Human Pose with Deep Neural Networks
Source: LINK.SPRINGER.COM

Abstract Most recent approaches to monocular 3D pose estimation rely on Deep Learning. They either train a Convolutional Neural Network to directly regress from an image to a 3D pose, which ignores the dependencies between human joints, or model these dependencies via a max-margin structured learning framework, which involves a high computational cost at inference time. In this paper, we introduce

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