2,214 research outputs found
Structure from Articulated Motion: Accurate and Stable Monocular 3D Reconstruction without Training Data
Recovery of articulated 3D structure from 2D observations is a challenging
computer vision problem with many applications. Current learning-based
approaches achieve state-of-the-art accuracy on public benchmarks but are
restricted to specific types of objects and motions covered by the training
datasets. Model-based approaches do not rely on training data but show lower
accuracy on these datasets. In this paper, we introduce a model-based method
called Structure from Articulated Motion (SfAM), which can recover multiple
object and motion types without training on extensive data collections. At the
same time, it performs on par with learning-based state-of-the-art approaches
on public benchmarks and outperforms previous non-rigid structure from motion
(NRSfM) methods. SfAM is built upon a general-purpose NRSfM technique while
integrating a soft spatio-temporal constraint on the bone lengths. We use
alternating optimization strategy to recover optimal geometry (i.e., bone
proportions) together with 3D joint positions by enforcing the bone lengths
consistency over a series of frames. SfAM is highly robust to noisy 2D
annotations, generalizes to arbitrary objects and does not rely on training
data, which is shown in extensive experiments on public benchmarks and real
video sequences. We believe that it brings a new perspective on the domain of
monocular 3D recovery of articulated structures, including human motion
capture.Comment: 21 pages, 8 figures, 2 table
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
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
GANerated Hands for Real-time 3D Hand Tracking from Monocular RGB
We address the highly challenging problem of real-time 3D hand tracking based
on a monocular RGB-only sequence. Our tracking method combines a convolutional
neural network with a kinematic 3D hand model, such that it generalizes well to
unseen data, is robust to occlusions and varying camera viewpoints, and leads
to anatomically plausible as well as temporally smooth hand motions. For
training our CNN we propose a novel approach for the synthetic generation of
training data that is based on a geometrically consistent image-to-image
translation network. To be more specific, we use a neural network that
translates synthetic images to "real" images, such that the so-generated images
follow the same statistical distribution as real-world hand images. For
training this translation network we combine an adversarial loss and a
cycle-consistency loss with a geometric consistency loss in order to preserve
geometric properties (such as hand pose) during translation. We demonstrate
that our hand tracking system outperforms the current state-of-the-art on
challenging RGB-only footage
Capturing Hands in Action using Discriminative Salient Points and Physics Simulation
Hand motion capture is a popular research field, recently gaining more
attention due to the ubiquity of RGB-D sensors. However, even most recent
approaches focus on the case of a single isolated hand. In this work, we focus
on hands that interact with other hands or objects and present a framework that
successfully captures motion in such interaction scenarios for both rigid and
articulated objects. Our framework combines a generative model with
discriminatively trained salient points to achieve a low tracking error and
with collision detection and physics simulation to achieve physically plausible
estimates even in case of occlusions and missing visual data. Since all
components are unified in a single objective function which is almost
everywhere differentiable, it can be optimized with standard optimization
techniques. Our approach works for monocular RGB-D sequences as well as setups
with multiple synchronized RGB cameras. For a qualitative and quantitative
evaluation, we captured 29 sequences with a large variety of interactions and
up to 150 degrees of freedom.Comment: Accepted for publication by the International Journal of Computer
Vision (IJCV) on 16.02.2016 (submitted on 17.10.14). A combination into a
single framework of an ECCV'12 multicamera-RGB and a monocular-RGBD GCPR'14
hand tracking paper with several extensions, additional experiments and
detail
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