578 research outputs found
Automated Markerless Extraction of Walking People Using Deformable Contour Models
We develop a new automated markerless motion capture system for the analysis of walking people. We employ global evidence gathering techniques guided by biomechanical analysis to robustly extract articulated motion. This forms a basis for new deformable contour models, using local image cues to capture shape and motion at a more detailed level. We extend the greedy snake formulation to include temporal constraints and occlusion modelling, increasing the capability of this technique when dealing with cluttered and self-occluding extraction targets. This approach is evaluated on a large database of indoor and outdoor video data, demonstrating fast and autonomous motion capture for walking people
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
MonoPerfCap: Human Performance Capture from Monocular Video
We present the first marker-less approach for temporally coherent 3D
performance capture of a human with general clothing from monocular video. Our
approach reconstructs articulated human skeleton motion as well as medium-scale
non-rigid surface deformations in general scenes. Human performance capture is
a challenging problem due to the large range of articulation, potentially fast
motion, and considerable non-rigid deformations, even from multi-view data.
Reconstruction from monocular video alone is drastically more challenging,
since strong occlusions and the inherent depth ambiguity lead to a highly
ill-posed reconstruction problem. We tackle these challenges by a novel
approach that employs sparse 2D and 3D human pose detections from a
convolutional neural network using a batch-based pose estimation strategy.
Joint recovery of per-batch motion allows to resolve the ambiguities of the
monocular reconstruction problem based on a low dimensional trajectory
subspace. In addition, we propose refinement of the surface geometry based on
fully automatically extracted silhouettes to enable medium-scale non-rigid
alignment. We demonstrate state-of-the-art performance capture results that
enable exciting applications such as video editing and free viewpoint video,
previously infeasible from monocular video. Our qualitative and quantitative
evaluation demonstrates that our approach significantly outperforms previous
monocular methods in terms of accuracy, robustness and scene complexity that
can be handled.Comment: Accepted to ACM TOG 2018, to be presented on SIGGRAPH 201
PhysCap: Physically Plausible Monocular 3D Motion Capture in Real Time
Marker-less 3D human motion capture from a single colour camera has seen
significant progress. However, it is a very challenging and severely ill-posed
problem. In consequence, even the most accurate state-of-the-art approaches
have significant limitations. Purely kinematic formulations on the basis of
individual joints or skeletons, and the frequent frame-wise reconstruction in
state-of-the-art methods greatly limit 3D accuracy and temporal stability
compared to multi-view or marker-based motion capture. Further, captured 3D
poses are often physically incorrect and biomechanically implausible, or
exhibit implausible environment interactions (floor penetration, foot skating,
unnatural body leaning and strong shifting in depth), which is problematic for
any use case in computer graphics. We, therefore, present PhysCap, the first
algorithm for physically plausible, real-time and marker-less human 3D motion
capture with a single colour camera at 25 fps. Our algorithm first captures 3D
human poses purely kinematically. To this end, a CNN infers 2D and 3D joint
positions, and subsequently, an inverse kinematics step finds space-time
coherent joint angles and global 3D pose. Next, these kinematic reconstructions
are used as constraints in a real-time physics-based pose optimiser that
accounts for environment constraints (e.g., collision handling and floor
placement), gravity, and biophysical plausibility of human postures. Our
approach employs a combination of ground reaction force and residual force for
plausible root control, and uses a trained neural network to detect foot
contact events in images. Our method captures physically plausible and
temporally stable global 3D human motion, without physically implausible
postures, floor penetrations or foot skating, from video in real time and in
general scenes. The video is available at
http://gvv.mpi-inf.mpg.de/projects/PhysCapComment: 16 pages, 11 figure
Capturing the sporting heroes of our past by extracting 3D movements from legacy video content
Sports are a key part of cultural identity, and it is necessary to preserve them as important intangible Cultural Heritage, especially the human motion techniques specific to individual sports. In this paper we present a method for extracting 3D athlete motion from video broadcast sources, providing an important tool for preserving the heritage represented by these movements. Broadcast videos include camera motion, multiple player interaction, occlusions and noise, presenting significant challenges to solve the reconstruction. The approach requires initial definition of some key-frames and setting of 2D key-points in those frames manually. Thereafter an automatic process estimates the poses and positions of the players in the key-frames, and in the frames between key-frames, taking into account collisions with the environment and human kinematic constraints. Initial results are extremely promising and this data could be used to analyze the sport's evolution over time, or even to generate animations for interactive applications
Non-contact measures to monitor hand movement of people with rheumatoid arthritis using a monocular RGB camera
Hand movements play an essential role in a personâs ability to interact with the environment. In hand biomechanics, the range of joint motion is a crucial metric to quantify changes due to degenerative pathologies, such as rheumatoid arthritis (RA). RA is a chronic condition where the immune system mistakenly attacks the joints, particularly those in the hands. Optoelectronic motion capture systems are gold-standard tools to quantify changes but are challenging to adopt outside laboratory settings. Deep learning executed on standard video data can capture RA participants in their natural environments, potentially supporting objectivity in remote consultation.
The three main research aims in this thesis were 1) to assess the extent to which current deep learning architectures, which have been validated for quantifying motion of other body segments, can be applied to hand kinematics using monocular RGB cameras, 2) to localise where in videos the hand motions of interest are to be found, 3) to assess the validity of 1) and 2) to determine disease status in RA.
First, hand kinematics for twelve healthy participants, captured with OpenPose were benchmarked against those captured using an optoelectronic system, showing acceptable instrument errors below 10°. Then, a gesture classifier was tested to segment video recordings of twenty-two healthy participants, achieving an accuracy of 93.5%. Finally, OpenPose and the classifier were applied to videos of RA participants performing hand exercises to determine disease status. The inferred disease activity exhibited agreement with the in-person ground truth in nine out of ten instances, outperforming virtual consultations, which agreed only six times out of ten.
These results demonstrate that this approach is more effective than estimated disease activity performed by human experts during video consultations. The end goal sets the foundation for a tool that RA participants can use to observe their disease activity from their home.Open Acces
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