Structured Prediction of 3D Human Pose with Deep Neural Networks

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, 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 a Deep Learning regression architecture for structured prediction of 3D human pose from monocular images that relies on an overcomplete autoencoder to learn a high-dimensional latent pose representation and account for joint dependencies. We demonstrate that our approach outperforms state-of-the-art ones both in terms of structure preservation and prediction accuracy.

Présenté à:
British Machine Vision Conference (BMVC), York, UK, September 19-22, 2016

Note: Le statut de ce fichier est:

 Notice créée le 2016-08-07, modifiée le 2019-12-05

tekin_bmvc16_abstract - Télécharger le documentPDF
tekin_bmvc16 - Télécharger le documentPDF
Évaluer ce document:

Rate this document:
(Pas encore évalué)