10,789 research outputs found
Recovering facial shape using a statistical model of surface normal direction
In this paper, we show how a statistical model of facial shape can be embedded within a shape-from-shading algorithm. We describe how facial shape can be captured using a statistical model of variations in surface normal direction. To construct this model, we make use of the azimuthal equidistant projection to map the distribution of surface normals from the polar representation on a unit sphere to Cartesian points on a local tangent plane. The distribution of surface normal directions is captured using the covariance matrix for the projected point positions. The eigenvectors of the covariance matrix define the modes of shape-variation in the fields of transformed surface normals. We show how this model can be trained using surface normal data acquired from range images and how to fit the model to intensity images of faces using constraints on the surface normal direction provided by Lambert's law. We demonstrate that the combination of a global statistical constraint and local irradiance constraint yields an efficient and accurate approach to facial shape recovery and is capable of recovering fine local surface details. We assess the accuracy of the technique on a variety of images with ground truth and real-world images
Learning to Reconstruct Texture-less Deformable Surfaces from a Single View
Recent years have seen the development of mature solutions for reconstructing
deformable surfaces from a single image, provided that they are relatively
well-textured. By contrast, recovering the 3D shape of texture-less surfaces
remains an open problem, and essentially relates to Shape-from-Shading. In this
paper, we introduce a data-driven approach to this problem. We introduce a
general framework that can predict diverse 3D representations, such as meshes,
normals, and depth maps. Our experiments show that meshes are ill-suited to
handle texture-less 3D reconstruction in our context. Furthermore, we
demonstrate that our approach generalizes well to unseen objects, and that it
yields higher-quality reconstructions than a state-of-the-art SfS technique,
particularly in terms of normal estimates. Our reconstructions accurately model
the fine details of the surfaces, such as the creases of a T-Shirt worn by a
person.Comment: Accepted to 3DV 201
DeLight-Net: Decomposing Reflectance Maps into Specular Materials and Natural Illumination
In this paper we are extracting surface reflectance and natural environmental
illumination from a reflectance map, i.e. from a single 2D image of a sphere of
one material under one illumination. This is a notoriously difficult problem,
yet key to various re-rendering applications. With the recent advances in
estimating reflectance maps from 2D images their further decomposition has
become increasingly relevant.
To this end, we propose a Convolutional Neural Network (CNN) architecture to
reconstruct both material parameters (i.e. Phong) as well as illumination (i.e.
high-resolution spherical illumination maps), that is solely trained on
synthetic data. We demonstrate that decomposition of synthetic as well as real
photographs of reflectance maps, both in High Dynamic Range (HDR), and, for the
first time, on Low Dynamic Range (LDR) as well. Results are compared to
previous approaches quantitatively as well as qualitatively in terms of
re-renderings where illumination, material, view or shape are changed.Comment: Stamatios Georgoulis and Konstantinos Rematas contributed equally to
this wor
Photometric Depth Super-Resolution
This study explores the use of photometric techniques (shape-from-shading and
uncalibrated photometric stereo) for upsampling the low-resolution depth map
from an RGB-D sensor to the higher resolution of the companion RGB image. A
single-shot variational approach is first put forward, which is effective as
long as the target's reflectance is piecewise-constant. It is then shown that
this dependency upon a specific reflectance model can be relaxed by focusing on
a specific class of objects (e.g., faces), and delegate reflectance estimation
to a deep neural network. A multi-shot strategy based on randomly varying
lighting conditions is eventually discussed. It requires no training or prior
on the reflectance, yet this comes at the price of a dedicated acquisition
setup. Both quantitative and qualitative evaluations illustrate the
effectiveness of the proposed methods on synthetic and real-world scenarios.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence
(T-PAMI), 2019. First three authors contribute equall
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
FML: Face Model Learning from Videos
Monocular image-based 3D reconstruction of faces is a long-standing problem
in computer vision. Since image data is a 2D projection of a 3D face, the
resulting depth ambiguity makes the problem ill-posed. Most existing methods
rely on data-driven priors that are built from limited 3D face scans. In
contrast, we propose multi-frame video-based self-supervised training of a deep
network that (i) learns a face identity model both in shape and appearance
while (ii) jointly learning to reconstruct 3D faces. Our face model is learned
using only corpora of in-the-wild video clips collected from the Internet. This
virtually endless source of training data enables learning of a highly general
3D face model. In order to achieve this, we propose a novel multi-frame
consistency loss that ensures consistent shape and appearance across multiple
frames of a subject's face, thus minimizing depth ambiguity. At test time we
can use an arbitrary number of frames, so that we can perform both monocular as
well as multi-frame reconstruction.Comment: CVPR 2019 (Oral). Video: https://www.youtube.com/watch?v=SG2BwxCw0lQ,
Project Page: https://gvv.mpi-inf.mpg.de/projects/FML19
CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images
With the powerfulness of convolution neural networks (CNN), CNN based face
reconstruction has recently shown promising performance in reconstructing
detailed face shape from 2D face images. The success of CNN-based methods
relies on a large number of labeled data. The state-of-the-art synthesizes such
data using a coarse morphable face model, which however has difficulty to
generate detailed photo-realistic images of faces (with wrinkles). This paper
presents a novel face data generation method. Specifically, we render a large
number of photo-realistic face images with different attributes based on
inverse rendering. Furthermore, we construct a fine-detailed face image dataset
by transferring different scales of details from one image to another. We also
construct a large number of video-type adjacent frame pairs by simulating the
distribution of real video data. With these nicely constructed datasets, we
propose a coarse-to-fine learning framework consisting of three convolutional
networks. The networks are trained for real-time detailed 3D face
reconstruction from monocular video as well as from a single image. Extensive
experimental results demonstrate that our framework can produce high-quality
reconstruction but with much less computation time compared to the
state-of-the-art. Moreover, our method is robust to pose, expression and
lighting due to the diversity of data.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine
Intelligence, 201
3D Face Reconstruction from Light Field Images: A Model-free Approach
Reconstructing 3D facial geometry from a single RGB image has recently
instigated wide research interest. However, it is still an ill-posed problem
and most methods rely on prior models hence undermining the accuracy of the
recovered 3D faces. In this paper, we exploit the Epipolar Plane Images (EPI)
obtained from light field cameras and learn CNN models that recover horizontal
and vertical 3D facial curves from the respective horizontal and vertical EPIs.
Our 3D face reconstruction network (FaceLFnet) comprises a densely connected
architecture to learn accurate 3D facial curves from low resolution EPIs. To
train the proposed FaceLFnets from scratch, we synthesize photo-realistic light
field images from 3D facial scans. The curve by curve 3D face estimation
approach allows the networks to learn from only 14K images of 80 identities,
which still comprises over 11 Million EPIs/curves. The estimated facial curves
are merged into a single pointcloud to which a surface is fitted to get the
final 3D face. Our method is model-free, requires only a few training samples
to learn FaceLFnet and can reconstruct 3D faces with high accuracy from single
light field images under varying poses, expressions and lighting conditions.
Comparison on the BU-3DFE and BU-4DFE datasets show that our method reduces
reconstruction errors by over 20% compared to recent state of the art
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