4,093 research outputs found
3D Shape Estimation from 2D Landmarks: A Convex Relaxation Approach
We investigate the problem of estimating the 3D shape of an object, given a
set of 2D landmarks in a single image. To alleviate the reconstruction
ambiguity, a widely-used approach is to confine the unknown 3D shape within a
shape space built upon existing shapes. While this approach has proven to be
successful in various applications, a challenging issue remains, i.e., the
joint estimation of shape parameters and camera-pose parameters requires to
solve a nonconvex optimization problem. The existing methods often adopt an
alternating minimization scheme to locally update the parameters, and
consequently the solution is sensitive to initialization. In this paper, we
propose a convex formulation to address this problem and develop an efficient
algorithm to solve the proposed convex program. We demonstrate the exact
recovery property of the proposed method, its merits compared to alternative
methods, and the applicability in human pose and car shape estimation.Comment: In Proceedings of CVPR 201
Efficient 3D Face Recognition with Gabor Patched Spectral Regression
In this paper, we utilize a novel framework for 3D face recognition, called 3D Gabor Patched Spectral Regression (3D GPSR), which can overcome some of the continuing challenges encountered with 2D or 3D facial images. In this active field, some obstacles, like expression variations, pose correction and data noise deteriorate the performance significantly. Our proposed system addresses these problems by first extracting the main facial area to remove irrelevant information corresponding to shoulders and necks. Pose correction is used to minimize the influence of large pose variations and then the normalized depth and gray images can be obtained. Due to better time-frequency characteristics and a distinctive biological background, the Gabor feature is extracted on depth images, known as 3D Gabor faces. Data noise is mainly caused by distorted meshes, varieties of subordinates and misalignment. To solve these problems, we introduce a Patched Spectral Regression strategy, which can make good use of the robustness and efficiency of accurate patched discriminant low-dimension features and minimize the effect of noise term. Computational analysis shows that spectral regression is much faster than the traditional approaches. Our experiments are based on the CASIA and FRGC 3D face databases which contain a huge number of challenging data. Experimental results show that our framework consistently outperforms the other existing methods with the distinctive characteristics of efficiency, robustness and generality
Geometric deep learning: going beyond Euclidean data
Many scientific fields study data with an underlying structure that is a
non-Euclidean space. Some examples include social networks in computational
social sciences, sensor networks in communications, functional networks in
brain imaging, regulatory networks in genetics, and meshed surfaces in computer
graphics. In many applications, such geometric data are large and complex (in
the case of social networks, on the scale of billions), and are natural targets
for machine learning techniques. In particular, we would like to use deep
neural networks, which have recently proven to be powerful tools for a broad
range of problems from computer vision, natural language processing, and audio
analysis. However, these tools have been most successful on data with an
underlying Euclidean or grid-like structure, and in cases where the invariances
of these structures are built into networks used to model them. Geometric deep
learning is an umbrella term for emerging techniques attempting to generalize
(structured) deep neural models to non-Euclidean domains such as graphs and
manifolds. The purpose of this paper is to overview different examples of
geometric deep learning problems and present available solutions, key
difficulties, applications, and future research directions in this nascent
field
Self-supervised Multi-level Face Model Learning for Monocular Reconstruction at over 250 Hz
The reconstruction of dense 3D models of face geometry and appearance from a
single image is highly challenging and ill-posed. To constrain the problem,
many approaches rely on strong priors, such as parametric face models learned
from limited 3D scan data. However, prior models restrict generalization of the
true diversity in facial geometry, skin reflectance and illumination. To
alleviate this problem, we present the first approach that jointly learns 1) a
regressor for face shape, expression, reflectance and illumination on the basis
of 2) a concurrently learned parametric face model. Our multi-level face model
combines the advantage of 3D Morphable Models for regularization with the
out-of-space generalization of a learned corrective space. We train end-to-end
on in-the-wild images without dense annotations by fusing a convolutional
encoder with a differentiable expert-designed renderer and a self-supervised
training loss, both defined at multiple detail levels. Our approach compares
favorably to the state-of-the-art in terms of reconstruction quality, better
generalizes to real world faces, and runs at over 250 Hz.Comment: CVPR 2018 (Oral). Project webpage:
https://gvv.mpi-inf.mpg.de/projects/FML
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