9,655 research outputs found
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
Neural Face Editing with Intrinsic Image Disentangling
Traditional face editing methods often require a number of sophisticated and
task specific algorithms to be applied one after the other --- a process that
is tedious, fragile, and computationally intensive. In this paper, we propose
an end-to-end generative adversarial network that infers a face-specific
disentangled representation of intrinsic face properties, including shape (i.e.
normals), albedo, and lighting, and an alpha matte. We show that this network
can be trained on "in-the-wild" images by incorporating an in-network
physically-based image formation module and appropriate loss functions. Our
disentangling latent representation allows for semantically relevant edits,
where one aspect of facial appearance can be manipulated while keeping
orthogonal properties fixed, and we demonstrate its use for a number of facial
editing applications.Comment: CVPR 2017 ora
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
Realistic Real-Time Rendering of Global Illumination and Hair through Machine Learning Precomputations
Over the last decade, machine learning has gained a lot of traction in many areas, and with the advent of new GPU models that include acceleration hardware for neural network inference, real-time applications have also started to take advantage of these algorithms.In general, machine learning and neural network methods are not designed to run at the speeds that are required for rendering in high-performance real-time environments, except for very specific and typically limited uses. For example, several methods have been developed recently for denoising of low quality pathtraced images, or to upsample images rendered at lower resolution, that can run in real-time.This thesis collects two methods that attempt to improve realistic scene rendering in such high-performance environments by using machine learning.Paper I presents a neural network application for compressing surface lightfields into a set of unconstrained spherical gaussians to render surfaces with global illumination in a real-time environment.Paper II describes a filter based on a small convolutional neural network that can be used to denoise hair rendered with stochastic transparency in real time
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
MoFA: Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction
In this work we propose a novel model-based deep convolutional autoencoder
that addresses the highly challenging problem of reconstructing a 3D human face
from a single in-the-wild color image. To this end, we combine a convolutional
encoder network with an expert-designed generative model that serves as
decoder. The core innovation is our new differentiable parametric decoder that
encapsulates image formation analytically based on a generative model. Our
decoder takes as input a code vector with exactly defined semantic meaning that
encodes detailed face pose, shape, expression, skin reflectance and scene
illumination. Due to this new way of combining CNN-based with model-based face
reconstruction, the CNN-based encoder learns to extract semantically meaningful
parameters from a single monocular input image. For the first time, a CNN
encoder and an expert-designed generative model can be trained end-to-end in an
unsupervised manner, which renders training on very large (unlabeled) real
world data feasible. The obtained reconstructions compare favorably to current
state-of-the-art approaches in terms of quality and richness of representation.Comment: International Conference on Computer Vision (ICCV) 2017 (Oral), 13
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