135,113 research outputs found
Articulated Clinician Detection Using 3D Pictorial Structures on RGB-D Data
Reliable human pose estimation (HPE) is essential to many clinical
applications, such as surgical workflow analysis, radiation safety monitoring
and human-robot cooperation. Proposed methods for the operating room (OR) rely
either on foreground estimation using a multi-camera system, which is a
challenge in real ORs due to color similarities and frequent illumination
changes, or on wearable sensors or markers, which are invasive and therefore
difficult to introduce in the room. Instead, we propose a novel approach based
on Pictorial Structures (PS) and on RGB-D data, which can be easily deployed in
real ORs. We extend the PS framework in two ways. First, we build robust and
discriminative part detectors using both color and depth images. We also
present a novel descriptor for depth images, called histogram of depth
differences (HDD). Second, we extend PS to 3D by proposing 3D pairwise
constraints and a new method that makes exact inference tractable. Our approach
is evaluated for pose estimation and clinician detection on a challenging RGB-D
dataset recorded in a busy operating room during live surgeries. We conduct
series of experiments to study the different part detectors in conjunction with
the various 2D or 3D pairwise constraints. Our comparisons demonstrate that 3D
PS with RGB-D part detectors significantly improves the results in a visually
challenging operating environment.Comment: The supplementary video is available at https://youtu.be/iabbGSqRSg
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
3D human pose estimation from depth maps using a deep combination of poses
Many real-world applications require the estimation of human body joints for
higher-level tasks as, for example, human behaviour understanding. In recent
years, depth sensors have become a popular approach to obtain three-dimensional
information. The depth maps generated by these sensors provide information that
can be employed to disambiguate the poses observed in two-dimensional images.
This work addresses the problem of 3D human pose estimation from depth maps
employing a Deep Learning approach. We propose a model, named Deep Depth Pose
(DDP), which receives a depth map containing a person and a set of predefined
3D prototype poses and returns the 3D position of the body joints of the
person. In particular, DDP is defined as a ConvNet that computes the specific
weights needed to linearly combine the prototypes for the given input. We have
thoroughly evaluated DDP on the challenging 'ITOP' and 'UBC3V' datasets, which
respectively depict realistic and synthetic samples, defining a new
state-of-the-art on them.Comment: Accepted for publication at "Journal of Visual Communication and
Image Representation
Learning from Synthetic Humans
Estimating human pose, shape, and motion from images and videos are
fundamental challenges with many applications. Recent advances in 2D human pose
estimation use large amounts of manually-labeled training data for learning
convolutional neural networks (CNNs). Such data is time consuming to acquire
and difficult to extend. Moreover, manual labeling of 3D pose, depth and motion
is impractical. In this work we present SURREAL (Synthetic hUmans foR REAL
tasks): a new large-scale dataset with synthetically-generated but realistic
images of people rendered from 3D sequences of human motion capture data. We
generate more than 6 million frames together with ground truth pose, depth
maps, and segmentation masks. We show that CNNs trained on our synthetic
dataset allow for accurate human depth estimation and human part segmentation
in real RGB images. Our results and the new dataset open up new possibilities
for advancing person analysis using cheap and large-scale synthetic data.Comment: Appears in: 2017 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2017). 9 page
V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map
Most of the existing deep learning-based methods for 3D hand and human pose
estimation from a single depth map are based on a common framework that takes a
2D depth map and directly regresses the 3D coordinates of keypoints, such as
hand or human body joints, via 2D convolutional neural networks (CNNs). The
first weakness of this approach is the presence of perspective distortion in
the 2D depth map. While the depth map is intrinsically 3D data, many previous
methods treat depth maps as 2D images that can distort the shape of the actual
object through projection from 3D to 2D space. This compels the network to
perform perspective distortion-invariant estimation. The second weakness of the
conventional approach is that directly regressing 3D coordinates from a 2D
image is a highly non-linear mapping, which causes difficulty in the learning
procedure. To overcome these weaknesses, we firstly cast the 3D hand and human
pose estimation problem from a single depth map into a voxel-to-voxel
prediction that uses a 3D voxelized grid and estimates the per-voxel likelihood
for each keypoint. We design our model as a 3D CNN that provides accurate
estimates while running in real-time. Our system outperforms previous methods
in almost all publicly available 3D hand and human pose estimation datasets and
placed first in the HANDS 2017 frame-based 3D hand pose estimation challenge.
The code is available in https://github.com/mks0601/V2V-PoseNet_RELEASE.Comment: HANDS 2017 Challenge Frame-based 3D Hand Pose Estimation Winner (ICCV
2017), Published at CVPR 201
LiveCap: Real-time Human Performance Capture from Monocular Video
We present the first real-time human performance capture approach that
reconstructs dense, space-time coherent deforming geometry of entire humans in
general everyday clothing from just a single RGB video. We propose a novel
two-stage analysis-by-synthesis optimization whose formulation and
implementation are designed for high performance. In the first stage, a skinned
template model is jointly fitted to background subtracted input video, 2D and
3D skeleton joint positions found using a deep neural network, and a set of
sparse facial landmark detections. In the second stage, dense non-rigid 3D
deformations of skin and even loose apparel are captured based on a novel
real-time capable algorithm for non-rigid tracking using dense photometric and
silhouette constraints. Our novel energy formulation leverages automatically
identified material regions on the template to model the differing non-rigid
deformation behavior of skin and apparel. The two resulting non-linear
optimization problems per-frame are solved with specially-tailored
data-parallel Gauss-Newton solvers. In order to achieve real-time performance
of over 25Hz, we design a pipelined parallel architecture using the CPU and two
commodity GPUs. Our method is the first real-time monocular approach for
full-body performance capture. Our method yields comparable accuracy with
off-line performance capture techniques, while being orders of magnitude
faster
Real-Time Human Motion Capture with Multiple Depth Cameras
Commonly used human motion capture systems require intrusive attachment of
markers that are visually tracked with multiple cameras. In this work we
present an efficient and inexpensive solution to markerless motion capture
using only a few Kinect sensors. Unlike the previous work on 3d pose estimation
using a single depth camera, we relax constraints on the camera location and do
not assume a co-operative user. We apply recent image segmentation techniques
to depth images and use curriculum learning to train our system on purely
synthetic data. Our method accurately localizes body parts without requiring an
explicit shape model. The body joint locations are then recovered by combining
evidence from multiple views in real-time. We also introduce a dataset of ~6
million synthetic depth frames for pose estimation from multiple cameras and
exceed state-of-the-art results on the Berkeley MHAD dataset.Comment: Accepted to computer robot vision 201
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