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 learn latent pose representation using an auto-encoder.
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