8,542 research outputs found
In the Wild Human Pose Estimation Using Explicit 2D Features and Intermediate 3D Representations
Convolutional Neural Network based approaches for monocular 3D human pose
estimation usually require a large amount of training images with 3D pose
annotations. While it is feasible to provide 2D joint annotations for large
corpora of in-the-wild images with humans, providing accurate 3D annotations to
such in-the-wild corpora is hardly feasible in practice. Most existing 3D
labelled data sets are either synthetically created or feature in-studio
images. 3D pose estimation algorithms trained on such data often have limited
ability to generalize to real world scene diversity. We therefore propose a new
deep learning based method for monocular 3D human pose estimation that shows
high accuracy and generalizes better to in-the-wild scenes. It has a network
architecture that comprises a new disentangled hidden space encoding of
explicit 2D and 3D features, and uses supervision by a new learned projection
model from predicted 3D pose. Our algorithm can be jointly trained on image
data with 3D labels and image data with only 2D labels. It achieves
state-of-the-art accuracy on challenging in-the-wild data.Comment: Accepted to CVPR 201
Human Pose Estimation using Global and Local Normalization
In this paper, we address the problem of estimating the positions of human
joints, i.e., articulated pose estimation. Recent state-of-the-art solutions
model two key issues, joint detection and spatial configuration refinement,
together using convolutional neural networks. Our work mainly focuses on
spatial configuration refinement by reducing variations of human poses
statistically, which is motivated by the observation that the scattered
distribution of the relative locations of joints e.g., the left wrist is
distributed nearly uniformly in a circular area around the left shoulder) makes
the learning of convolutional spatial models hard. We present a two-stage
normalization scheme, human body normalization and limb normalization, to make
the distribution of the relative joint locations compact, resulting in easier
learning of convolutional spatial models and more accurate pose estimation. In
addition, our empirical results show that incorporating multi-scale supervision
and multi-scale fusion into the joint detection network is beneficial.
Experiment results demonstrate that our method consistently outperforms
state-of-the-art methods on the benchmarks.Comment: ICCV201
In the Wild Human Pose Estimation Using Explicit 2D Features and Intermediate 3D Representations
Convolutional Neural Network based approaches for monocular 3D human pose estimation usually require a large amount of training images with 3D pose annotations. While it is feasible to provide 2D joint annotations for large corpora of in-the-wild images with humans, providing accurate 3D annotations to such in-the-wild corpora is hardly feasible in practice. Most existing 3D labelled data sets are either synthetically created or feature in-studio images. 3D pose estimation algorithms trained on such data often have limited ability to generalize to real world scene diversity. We therefore propose a new deep learning based method for monocular 3D human pose estimation that shows high accuracy and generalizes better to in-the-wild scenes. It has a network architecture that comprises a new disentangled hidden space encoding of explicit 2D and 3D features, and uses supervision by a new learned projection model from predicted 3D pose. Our algorithm can be jointly trained on image data with 3D labels and image data with only 2D labels. It achieves state-of-the-art accuracy on challenging in-the-wild data
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