Most recent approaches to monocular 3D pose estimation rely on Deep Learning. They either train a Convolutional Neural Network to directly regress from image to 3D pose [3] , which ignores the dependencies between human joints, or model these dependencies via a max-margin structured learning framework [4] , which involves a high computational cost at inference time. In this paper, we introduce a Deep Learning regression architecture for structured prediction of 3D pose from monocular images that relies on an overcomplete auto-encoder to learn a high-dimensional latent pose representation and account for joint dependencies.
Tekin et al. REF proposed a deep learning regression for structure prediction of 3D human pose from monocular image.
Structured Prediction of 3D Human Pose with Deep Neural Networks
Procedings of the British Machine Vision Conference 2016
Procedings of the British Machine Vision Conference 2016
Computer Science