4,348 research outputs found
DeepPose: Human Pose Estimation via Deep Neural Networks
We propose a method for human pose estimation based on Deep Neural Networks
(DNNs). The pose estimation is formulated as a DNN-based regression problem
towards body joints. We present a cascade of such DNN regressors which results
in high precision pose estimates. The approach has the advantage of reasoning
about pose in a holistic fashion and has a simple but yet powerful formulation
which capitalizes on recent advances in Deep Learning. We present a detailed
empirical analysis with state-of-art or better performance on four academic
benchmarks of diverse real-world images.Comment: IEEE Conference on Computer Vision and Pattern Recognition, 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
Combining Local Appearance and Holistic View: Dual-Source Deep Neural Networks for Human Pose Estimation
We propose a new learning-based method for estimating 2D human pose from a
single image, using Dual-Source Deep Convolutional Neural Networks (DS-CNN).
Recently, many methods have been developed to estimate human pose by using pose
priors that are estimated from physiologically inspired graphical models or
learned from a holistic perspective. In this paper, we propose to integrate
both the local (body) part appearance and the holistic view of each local part
for more accurate human pose estimation. Specifically, the proposed DS-CNN
takes a set of image patches (category-independent object proposals for
training and multi-scale sliding windows for testing) as the input and then
learns the appearance of each local part by considering their holistic views in
the full body. Using DS-CNN, we achieve both joint detection, which determines
whether an image patch contains a body joint, and joint localization, which
finds the exact location of the joint in the image patch. Finally, we develop
an algorithm to combine these joint detection/localization results from all the
image patches for estimating the human pose. The experimental results show the
effectiveness of the proposed method by comparing to the state-of-the-art
human-pose estimation methods based on pose priors that are estimated from
physiologically inspired graphical models or learned from a holistic
perspective.Comment: CVPR 201
Cascaded 3D Full-body Pose Regression from Single Depth Image at 100 FPS
There are increasing real-time live applications in virtual reality, where it
plays an important role in capturing and retargetting 3D human pose. But it is
still challenging to estimate accurate 3D pose from consumer imaging devices
such as depth camera. This paper presents a novel cascaded 3D full-body pose
regression method to estimate accurate pose from a single depth image at 100
fps. The key idea is to train cascaded regressors based on Gradient Boosting
algorithm from pre-recorded human motion capture database. By incorporating
hierarchical kinematics model of human pose into the learning procedure, we can
directly estimate accurate 3D joint angles instead of joint positions. The
biggest advantage of this model is that the bone length can be preserved during
the whole 3D pose estimation procedure, which leads to more effective features
and higher pose estimation accuracy. Our method can be used as an
initialization procedure when combining with tracking methods. We demonstrate
the power of our method on a wide range of synthesized human motion data from
CMU mocap database, Human3.6M dataset and real human movements data captured in
real time. In our comparison against previous 3D pose estimation methods and
commercial system such as Kinect 2017, we achieve the state-of-the-art
accuracy
Robust Estimation of 3D Human Poses from a Single Image
Human pose estimation is a key step to action recognition. We propose a
method of estimating 3D human poses from a single image, which works in
conjunction with an existing 2D pose/joint detector. 3D pose estimation is
challenging because multiple 3D poses may correspond to the same 2D pose after
projection due to the lack of depth information. Moreover, current 2D pose
estimators are usually inaccurate which may cause errors in the 3D estimation.
We address the challenges in three ways: (i) We represent a 3D pose as a linear
combination of a sparse set of bases learned from 3D human skeletons. (ii) We
enforce limb length constraints to eliminate anthropomorphically implausible
skeletons. (iii) We estimate a 3D pose by minimizing the -norm error
between the projection of the 3D pose and the corresponding 2D detection. The
-norm loss term is robust to inaccurate 2D joint estimations. We use the
alternating direction method (ADM) to solve the optimization problem
efficiently. Our approach outperforms the state-of-the-arts on three benchmark
datasets
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