21,893 research outputs found
Learning Human Pose Estimation Features with Convolutional Networks
This paper introduces a new architecture for human pose estimation using a
multi- layer convolutional network architecture and a modified learning
technique that learns low-level features and higher-level weak spatial models.
Unconstrained human pose estimation is one of the hardest problems in computer
vision, and our new architecture and learning schema shows significant
improvement over the current state-of-the-art results. The main contribution of
this paper is showing, for the first time, that a specific variation of deep
learning is able to outperform all existing traditional architectures on this
task. The paper also discusses several lessons learned while researching
alternatives, most notably, that it is possible to learn strong low-level
feature detectors on features that might even just cover a few pixels in the
image. Higher-level spatial models improve somewhat the overall result, but to
a much lesser extent then expected. Many researchers previously argued that the
kinematic structure and top-down information is crucial for this domain, but
with our purely bottom up, and weak spatial model, we could improve other more
complicated architectures that currently produce the best results. This mirrors
what many other researchers, like those in the speech recognition, object
recognition, and other domains have experienced
Heterogeneous Multi-task Learning for Human Pose Estimation with Deep Convolutional Neural Network
We propose an heterogeneous multi-task learning framework for human pose
estimation from monocular image with deep convolutional neural network. In
particular, we simultaneously learn a pose-joint regressor and a sliding-window
body-part detector in a deep network architecture. We show that including the
body-part detection task helps to regularize the network, directing it to
converge to a good solution. We report competitive and state-of-art results on
several data sets. We also empirically show that the learned neurons in the
middle layer of our network are tuned to localized body parts
Semantic Graph Convolutional Networks for 3D Human Pose Regression
In this paper, we study the problem of learning Graph Convolutional Networks
(GCNs) for regression. Current architectures of GCNs are limited to the small
receptive field of convolution filters and shared transformation matrix for
each node. To address these limitations, we propose Semantic Graph
Convolutional Networks (SemGCN), a novel neural network architecture that
operates on regression tasks with graph-structured data. SemGCN learns to
capture semantic information such as local and global node relationships, which
is not explicitly represented in the graph. These semantic relationships can be
learned through end-to-end training from the ground truth without additional
supervision or hand-crafted rules. We further investigate applying SemGCN to 3D
human pose regression. Our formulation is intuitive and sufficient since both
2D and 3D human poses can be represented as a structured graph encoding the
relationships between joints in the skeleton of a human body. We carry out
comprehensive studies to validate our method. The results prove that SemGCN
outperforms state of the art while using 90% fewer parameters.Comment: In CVPR 2019 (13 pages including supplementary material). The code
can be found at https://github.com/garyzhao/SemGC
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