29,617 research outputs found
VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera
We present the first real-time method to capture the full global 3D skeletal
pose of a human in a stable, temporally consistent manner using a single RGB
camera. Our method combines a new convolutional neural network (CNN) based pose
regressor with kinematic skeleton fitting. Our novel fully-convolutional pose
formulation regresses 2D and 3D joint positions jointly in real time and does
not require tightly cropped input frames. A real-time kinematic skeleton
fitting method uses the CNN output to yield temporally stable 3D global pose
reconstructions on the basis of a coherent kinematic skeleton. This makes our
approach the first monocular RGB method usable in real-time applications such
as 3D character control---thus far, the only monocular methods for such
applications employed specialized RGB-D cameras. Our method's accuracy is
quantitatively on par with the best offline 3D monocular RGB pose estimation
methods. Our results are qualitatively comparable to, and sometimes better
than, results from monocular RGB-D approaches, such as the Kinect. However, we
show that our approach is more broadly applicable than RGB-D solutions, i.e. it
works for outdoor scenes, community videos, and low quality commodity RGB
cameras.Comment: Accepted to SIGGRAPH 201
PifPaf: Composite Fields for Human Pose Estimation
We propose a new bottom-up method for multi-person 2D human pose estimation
that is particularly well suited for urban mobility such as self-driving cars
and delivery robots. The new method, PifPaf, uses a Part Intensity Field (PIF)
to localize body parts and a Part Association Field (PAF) to associate body
parts with each other to form full human poses. Our method outperforms previous
methods at low resolution and in crowded, cluttered and occluded scenes thanks
to (i) our new composite field PAF encoding fine-grained information and (ii)
the choice of Laplace loss for regressions which incorporates a notion of
uncertainty. Our architecture is based on a fully convolutional, single-shot,
box-free design. We perform on par with the existing state-of-the-art bottom-up
method on the standard COCO keypoint task and produce state-of-the-art results
on a modified COCO keypoint task for the transportation domain.Comment: CVPR 201
Single camera pose estimation using Bayesian filtering and Kinect motion priors
Traditional approaches to upper body pose estimation using monocular vision
rely on complex body models and a large variety of geometric constraints. We
argue that this is not ideal and somewhat inelegant as it results in large
processing burdens, and instead attempt to incorporate these constraints
through priors obtained directly from training data. A prior distribution
covering the probability of a human pose occurring is used to incorporate
likely human poses. This distribution is obtained offline, by fitting a
Gaussian mixture model to a large dataset of recorded human body poses, tracked
using a Kinect sensor. We combine this prior information with a random walk
transition model to obtain an upper body model, suitable for use within a
recursive Bayesian filtering framework. Our model can be viewed as a mixture of
discrete Ornstein-Uhlenbeck processes, in that states behave as random walks,
but drift towards a set of typically observed poses. This model is combined
with measurements of the human head and hand positions, using recursive
Bayesian estimation to incorporate temporal information. Measurements are
obtained using face detection and a simple skin colour hand detector, trained
using the detected face. The suggested model is designed with analytical
tractability in mind and we show that the pose tracking can be
Rao-Blackwellised using the mixture Kalman filter, allowing for computational
efficiency while still incorporating bio-mechanical properties of the upper
body. In addition, the use of the proposed upper body model allows reliable
three-dimensional pose estimates to be obtained indirectly for a number of
joints that are often difficult to detect using traditional object recognition
strategies. Comparisons with Kinect sensor results and the state of the art in
2D pose estimation highlight the efficacy of the proposed approach.Comment: 25 pages, Technical report, related to Burke and Lasenby, AMDO 2014
conference paper. Code sample: https://github.com/mgb45/SignerBodyPose Video:
https://www.youtube.com/watch?v=dJMTSo7-uF
Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image
We describe the first method to automatically estimate the 3D pose of the
human body as well as its 3D shape from a single unconstrained image. We
estimate a full 3D mesh and show that 2D joints alone carry a surprising amount
of information about body shape. The problem is challenging because of the
complexity of the human body, articulation, occlusion, clothing, lighting, and
the inherent ambiguity in inferring 3D from 2D. To solve this, we first use a
recently published CNN-based method, DeepCut, to predict (bottom-up) the 2D
body joint locations. We then fit (top-down) a recently published statistical
body shape model, called SMPL, to the 2D joints. We do so by minimizing an
objective function that penalizes the error between the projected 3D model
joints and detected 2D joints. Because SMPL captures correlations in human
shape across the population, we are able to robustly fit it to very little
data. We further leverage the 3D model to prevent solutions that cause
interpenetration. We evaluate our method, SMPLify, on the Leeds Sports,
HumanEva, and Human3.6M datasets, showing superior pose accuracy with respect
to the state of the art.Comment: To appear in ECCV 201
Evaluating Example-based Pose Estimation: Experiments on the HumanEva Sets
We present an example-based approach to pose recovery, using histograms of oriented gradients as image descriptors. Tests on the HumanEva-I and HumanEva-II data sets provide us insight into the strengths and limitations of an example-based approach. We report mean relative 3D errors of approximately 65 mm per joint on HumanEva-I, and 175 mm on HumanEva-II. We discuss our results using single and multiple views. Also, we perform experiments to assess the algorithm’s generalization to unseen subjects, actions and viewpoints. We plan to incorporate the temporal aspect of human motion analysis to reduce orientation ambiguities, and increase the pose recovery accuracy
XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera
We present a real-time approach for multi-person 3D motion capture at over 30
fps using a single RGB camera. It operates successfully in generic scenes which
may contain occlusions by objects and by other people. Our method operates in
subsequent stages. The first stage is a convolutional neural network (CNN) that
estimates 2D and 3D pose features along with identity assignments for all
visible joints of all individuals.We contribute a new architecture for this
CNN, called SelecSLS Net, that uses novel selective long and short range skip
connections to improve the information flow allowing for a drastically faster
network without compromising accuracy. In the second stage, a fully connected
neural network turns the possibly partial (on account of occlusion) 2Dpose and
3Dpose features for each subject into a complete 3Dpose estimate per
individual. The third stage applies space-time skeletal model fitting to the
predicted 2D and 3D pose per subject to further reconcile the 2D and 3D pose,
and enforce temporal coherence. Our method returns the full skeletal pose in
joint angles for each subject. This is a further key distinction from previous
work that do not produce joint angle results of a coherent skeleton in real
time for multi-person scenes. The proposed system runs on consumer hardware at
a previously unseen speed of more than 30 fps given 512x320 images as input
while achieving state-of-the-art accuracy, which we will demonstrate on a range
of challenging real-world scenes.Comment: To appear in ACM Transactions on Graphics (SIGGRAPH) 202
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