2 research outputs found
Landmark Weighting for 3DMM Shape Fitting
Human face is a 3D object with shape and surface texture. 3D Morphable Model
(3DMM) is a powerful tool for reconstructing the 3D face from a single 2D face
image. In the shape fitting process, 3DMM estimates the correspondence between
2D and 3D landmarks. Most traditional 3DMM fitting methods fail to reconstruct
an accurate model because face shape fitting is a difficult non-linear
optimization problem. In this paper we show that landmark weighting is
instrumental to improve the accuracy of shape reconstruction and propose a
novel 3D Morphable Model Fitting method. Different from previous works that
treat all landmarks equally, we take into consideration the estimated errors
for each pair of 2D and 3D corresponding landmarks. The landmark points are
weighted in the optimization cost function based on these errors. Obviously,
these landmarks have different semantics because they locate on different
facial components. In the context of the solution of fitting is approximated,
there are deviations in landmarks matching. However, these landmarks with
different semantics have different effects on reconstructing 3D faces. Thus, it
is necessary to consider each landmark individually. To our knowledge, we are
the first to analyze each feature point for 3D face reconstruction by 3DMM. The
weight is adaptive with the estimation residuals of landmarks. Experimental
results show that the proposed method significantly reduces the reconstruction
error and improves the authenticity of the 3D model expression.Comment: 7 pages, 7 figure
Pose Invariant 3D Face Reconstruction
3D face reconstruction is an important task in the field of computer vision.
Although 3D face reconstruction has being developing rapidly in recent years,
it is still a challenge for face reconstruction under large pose. That is
because much of the information about a face in a large pose will be
unknowable. In order to address this issue, this paper proposes a novel 3D face
reconstruction algorithm (PIFR) based on 3D Morphable Model (3DMM). After input
a single face image, it generates a frontal image by normalizing the image.
Then we set weighted sum of the 3D parameters of the two images. Our method
solves the problem of face reconstruction of a single image of a traditional
method in a large pose, works on arbitrary Pose and Expressions, greatly
improves the accuracy of reconstruction. Experiments on the challenging AFW,
LFPW and AFLW database show that our algorithm significantly improves the
accuracy of 3D face reconstruction even under extreme poses .Comment: 8 page