30,064 research outputs found
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
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
Simultaneous Hand Pose and Skeleton Bone-Lengths Estimation from a Single Depth Image
Articulated hand pose estimation is a challenging task for human-computer
interaction. The state-of-the-art hand pose estimation algorithms work only
with one or a few subjects for which they have been calibrated or trained.
Particularly, the hybrid methods based on learning followed by model fitting or
model based deep learning do not explicitly consider varying hand shapes and
sizes. In this work, we introduce a novel hybrid algorithm for estimating the
3D hand pose as well as bone-lengths of the hand skeleton at the same time,
from a single depth image. The proposed CNN architecture learns hand pose
parameters and scale parameters associated with the bone-lengths
simultaneously. Subsequently, a new hybrid forward kinematics layer employs
both parameters to estimate 3D joint positions of the hand. For end-to-end
training, we combine three public datasets NYU, ICVL and MSRA-2015 in one
unified format to achieve large variation in hand shapes and sizes. Among
hybrid methods, our method shows improved accuracy over the state-of-the-art on
the combined dataset and the ICVL dataset that contain multiple subjects. Also,
our algorithm is demonstrated to work well with unseen images.Comment: This paper has been accepted and presented in 3DV-2017 conference
held at Qingdao, China. http://irc.cs.sdu.edu.cn/3dv
MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation
In this work, we propose a novel and efficient method for articulated human
pose estimation in videos using a convolutional network architecture, which
incorporates both color and motion features. We propose a new human body pose
dataset, FLIC-motion, that extends the FLIC dataset with additional motion
features. We apply our architecture to this dataset and report significantly
better performance than current state-of-the-art pose detection systems
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