306 research outputs found
Motion Imitation Based on Sparsely Sampled Correspondence
Existing techniques for motion imitation often suffer a certain level of
latency due to their computational overhead or a large set of correspondence
samples to search. To achieve real-time imitation with small latency, we
present a framework in this paper to reconstruct motion on humanoids based on
sparsely sampled correspondence. The imitation problem is formulated as finding
the projection of a point from the configuration space of a human's poses into
the configuration space of a humanoid. An optimal projection is defined as the
one that minimizes a back-projected deviation among a group of candidates,
which can be determined in a very efficient way. Benefited from this
formulation, effective projections can be obtained by using sparse
correspondence. Methods for generating these sparse correspondence samples have
also been introduced. Our method is evaluated by applying the human's motion
captured by a RGB-D sensor to a humanoid in real-time. Continuous motion can be
realized and used in the example application of tele-operation.Comment: 8 pages, 8 figures, technical repor
Collision-Aware Fast Simulation for Soft Robots by Optimization-Based Geometric Computing
Soft robots can safely interact with environments because of their mechanical
compliance. Self-collision is also employed in the modern design of soft robots
to enhance their performance during different tasks. However, developing an
efficient and reliable simulator that can handle the collision response well,
is still a challenging task in the research of soft robotics. This paper
presents a collision-aware simulator based on geometric optimization, in which
we develop a highly efficient and realistic collision checking / response model
incorporating a hyperelastic material property. Both actuated deformation and
collision response for soft robots are formulated as geometry-based objectives.
The collision-free body of a soft robot can be obtained by minimizing the
geometry-based objective function. Unlike the FEA-based physical simulation,
the proposed pipeline performs a much lower computational cost. Moreover,
adaptive remeshing is applied to achieve the improvement of the convergence
when dealing with soft robots that have large volume variations. Experimental
tests are conducted on different soft robots to verify the performance of our
approach
Concise and Effective Network for 3D Human Modeling from Orthogonal Silhouettes
In this paper, we revisit the problem of 3D human modeling from two
orthogonal silhouettes of individuals (i.e., front and side views). Different
from our prior work {\cite{wang2003virtual}}, a supervised learning approach
based on \textit{convolutional neural network} (CNN) is investigated to solve
the problem by establishing a mapping function that can effectively extract
features from two silhouettes and fuse them into coefficients in the shape
space of human bodies. A new CNN structure is proposed in our work to exact not
only the discriminative features of front and side views and also their mixed
features for the mapping function. 3D human models with high accuracy are
synthesized from coefficients generated by the mapping function. Existing CNN
approaches for 3D human modeling usually learn a large number of parameters
(from {8.5M} to {355.4M}) from two binary images. Differently, we investigate a
new network architecture and conduct the samples on silhouettes as input. As a
consequence, more accurate models can be generated by our network with only
{2.4M} coefficients. The training of our network is conducted on samples
obtained by augmenting a publicly accessible dataset. Learning transfer by
using datasets with a smaller number of scanned models is applied to our
network to enable the function of generating results with gender-oriented (or
geographical) patterns
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