17,424 research outputs found
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
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
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
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