2,924 research outputs found
Multilinear Wavelets: A Statistical Shape Space for Human Faces
We present a statistical model for D human faces in varying expression,
which decomposes the surface of the face using a wavelet transform, and learns
many localized, decorrelated multilinear models on the resulting coefficients.
Using this model we are able to reconstruct faces from noisy and occluded D
face scans, and facial motion sequences. Accurate reconstruction of face shape
is important for applications such as tele-presence and gaming. The localized
and multi-scale nature of our model allows for recovery of fine-scale detail
while retaining robustness to severe noise and occlusion, and is
computationally efficient and scalable. We validate these properties
experimentally on challenging data in the form of static scans and motion
sequences. We show that in comparison to a global multilinear model, our model
better preserves fine detail and is computationally faster, while in comparison
to a localized PCA model, our model better handles variation in expression, is
faster, and allows us to fix identity parameters for a given subject.Comment: 10 pages, 7 figures; accepted to ECCV 201
Differentiable Subdivision Surface Fitting
In this paper, we present a powerful differentiable surface fitting technique
to derive a compact surface representation for a given dense point cloud or
mesh, with application in the domains of graphics and CAD/CAM. We have chosen
the Loop subdivision surface, which in the limit yields the smooth surface
underlying the point cloud, and can handle complex surface topology better than
other popular compact representations, such as NURBS. The principal idea is to
fit the Loop subdivision surface not directly to the point cloud, but to the
IMLS (implicit moving least squares) surface defined over the point cloud. As
both Loop subdivision and IMLS have analytical expressions, we are able to
formulate the problem as an unconstrained minimization problem of a completely
differentiable function that can be solved with standard numerical solvers.
Differentiability enables us to integrate the subdivision surface into any deep
learning method for point clouds or meshes. We demonstrate the versatility and
potential of this approach by using it in conjunction with a differentiable
renderer to robustly reconstruct compact surface representations of
spatial-temporal sequences of dense meshes
A Study On Applications And Techniques Of Surface Re- Construction
This paper describes a general method for automatic reconstruction of accurate, concise, piecewise smooth surfaces from unorganized 3D points. Instances of surface reconstruction arise in numerous scientific and engineering applications, including reverseengineering, the automatic generation of CAD models from physical objects etc. Previous surface reconstruction methods have typically required additional knowledge, such as structure in the data, known surface genus, or orientation information. In contrast, the method outlined in this paper requires only the 3D coordinates of the data points. From the data, the method is able to automatically infer the topological type of the surface, its geometry, and the presence and location of features such as boundaries, creases, and corners. The surface reconstruction method has three major phases: Initial surface estimation, Mesh optimization, and piecewise smooth surface optimization. In this paper emphasis has been given on the initial surface estimation
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