11,065 research outputs found
Finite Element Based Tracking of Deforming Surfaces
We present an approach to robustly track the geometry of an object that
deforms over time from a set of input point clouds captured from a single
viewpoint. The deformations we consider are caused by applying forces to known
locations on the object's surface. Our method combines the use of prior
information on the geometry of the object modeled by a smooth template and the
use of a linear finite element method to predict the deformation. This allows
the accurate reconstruction of both the observed and the unobserved sides of
the object. We present tracking results for noisy low-quality point clouds
acquired by either a stereo camera or a depth camera, and simulations with
point clouds corrupted by different error terms. We show that our method is
also applicable to large non-linear deformations.Comment: additional experiment
Trajectory Deformations from Physical Human-Robot Interaction
Robots are finding new applications where physical interaction with a human
is necessary: manufacturing, healthcare, and social tasks. Accordingly, the
field of physical human-robot interaction (pHRI) has leveraged impedance
control approaches, which support compliant interactions between human and
robot. However, a limitation of traditional impedance control is that---despite
provisions for the human to modify the robot's current trajectory---the human
cannot affect the robot's future desired trajectory through pHRI. In this
paper, we present an algorithm for physically interactive trajectory
deformations which, when combined with impedance control, allows the human to
modulate both the actual and desired trajectories of the robot. Unlike related
works, our method explicitly deforms the future desired trajectory based on
forces applied during pHRI, but does not require constant human guidance. We
present our approach and verify that this method is compatible with traditional
impedance control. Next, we use constrained optimization to derive the
deformation shape. Finally, we describe an algorithm for real time
implementation, and perform simulations to test the arbitration parameters.
Experimental results demonstrate reduction in the human's effort and
improvement in the movement quality when compared to pHRI with impedance
control alone
Deformable Prototypes for Encoding Shape Categories in Image Databases
We describe a method for shape-based image database search that uses deformable prototypes to represent categories. Rather than directly comparing a candidate shape with all shape entries in the database, shapes are compared in terms of the types of nonrigid deformations (differences) that relate them to a small subset of representative prototypes. To solve the shape correspondence and alignment problem, we employ the technique of modal matching, an information-preserving shape decomposition for matching, describing, and comparing shapes despite sensor variations and nonrigid deformations. In modal matching, shape is decomposed into an ordered basis of orthogonal principal components. We demonstrate the utility of this approach for shape comparison in 2-D image databases.Office of Naval Research (Young Investigator Award N00014-06-1-0661
Expressive Body Capture: 3D Hands, Face, and Body from a Single Image
To facilitate the analysis of human actions, interactions and emotions, we
compute a 3D model of human body pose, hand pose, and facial expression from a
single monocular image. To achieve this, we use thousands of 3D scans to train
a new, unified, 3D model of the human body, SMPL-X, that extends SMPL with
fully articulated hands and an expressive face. Learning to regress the
parameters of SMPL-X directly from images is challenging without paired images
and 3D ground truth. Consequently, we follow the approach of SMPLify, which
estimates 2D features and then optimizes model parameters to fit the features.
We improve on SMPLify in several significant ways: (1) we detect 2D features
corresponding to the face, hands, and feet and fit the full SMPL-X model to
these; (2) we train a new neural network pose prior using a large MoCap
dataset; (3) we define a new interpenetration penalty that is both fast and
accurate; (4) we automatically detect gender and the appropriate body models
(male, female, or neutral); (5) our PyTorch implementation achieves a speedup
of more than 8x over Chumpy. We use the new method, SMPLify-X, to fit SMPL-X to
both controlled images and images in the wild. We evaluate 3D accuracy on a new
curated dataset comprising 100 images with pseudo ground-truth. This is a step
towards automatic expressive human capture from monocular RGB data. The models,
code, and data are available for research purposes at
https://smpl-x.is.tue.mpg.de.Comment: To appear in CVPR 201
Framework for a low-cost intra-operative image-guided neuronavigator including brain shift compensation
In this paper we present a methodology to address the problem of brain tissue
deformation referred to as 'brain-shift'. This deformation occurs throughout a
neurosurgery intervention and strongly alters the accuracy of the
neuronavigation systems used to date in clinical routine which rely solely on
pre-operative patient imaging to locate the surgical target, such as a tumour
or a functional area. After a general description of the framework of our
intra-operative image-guided system, we describe a procedure to generate
patient specific finite element meshes of the brain and propose a biomechanical
model which can take into account tissue deformations and surgical procedures
that modify the brain structure, like tumour or tissue resection
Calipso: Physics-based Image and Video Editing through CAD Model Proxies
We present Calipso, an interactive method for editing images and videos in a
physically-coherent manner. Our main idea is to realize physics-based
manipulations by running a full physics simulation on proxy geometries given by
non-rigidly aligned CAD models. Running these simulations allows us to apply
new, unseen forces to move or deform selected objects, change physical
parameters such as mass or elasticity, or even add entire new objects that
interact with the rest of the underlying scene. In Calipso, the user makes
edits directly in 3D; these edits are processed by the simulation and then
transfered to the target 2D content using shape-to-image correspondences in a
photo-realistic rendering process. To align the CAD models, we introduce an
efficient CAD-to-image alignment procedure that jointly minimizes for rigid and
non-rigid alignment while preserving the high-level structure of the input
shape. Moreover, the user can choose to exploit image flow to estimate scene
motion, producing coherent physical behavior with ambient dynamics. We
demonstrate Calipso's physics-based editing on a wide range of examples
producing myriad physical behavior while preserving geometric and visual
consistency.Comment: 11 page
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