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- Title
Learning Latent Representations of 3D Human Pose with Deep Neural Networks.
- Authors
Katircioglu, Isinsu; Tekin, Bugra; Salzmann, Mathieu; Lepetit, Vincent; Fua, Pascal
- 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 a Deep Learning regression architecture for structured prediction of 3D human pose from monocular images or 2D joint location heatmaps that relies on an overcomplete autoencoder to learn a high-dimensional latent pose representation and accounts for joint dependencies. We further propose an efficient Long Short-Term Memory network to enforce temporal consistency on 3D pose predictions. We demonstrate that our approach achieves state-of-the-art performance both in terms of structure preservation and prediction accuracy on standard 3D human pose estimation benchmarks.
- Subjects
DEEP learning; ARTIFICIAL neural networks; POSE estimation (Computer vision); COMPUTER vision; IMAGE reconstruction
- Publication
International Journal of Computer Vision, 2018, Vol 126, Issue 12, p1326
- ISSN
0920-5691
- Publication type
Article
- DOI
10.1007/s11263-018-1066-6