2,121 research outputs found
A 3D Face Modelling Approach for Pose-Invariant Face Recognition in a Human-Robot Environment
Face analysis techniques have become a crucial component of human-machine
interaction in the fields of assistive and humanoid robotics. However, the
variations in head-pose that arise naturally in these environments are still a
great challenge. In this paper, we present a real-time capable 3D face
modelling framework for 2D in-the-wild images that is applicable for robotics.
The fitting of the 3D Morphable Model is based exclusively on automatically
detected landmarks. After fitting, the face can be corrected in pose and
transformed back to a frontal 2D representation that is more suitable for face
recognition. We conduct face recognition experiments with non-frontal images
from the MUCT database and uncontrolled, in the wild images from the PaSC
database, the most challenging face recognition database to date, showing an
improved performance. Finally, we present our SCITOS G5 robot system, which
incorporates our framework as a means of image pre-processing for face
analysis
3D Face Tracking and Texture Fusion in the Wild
We present a fully automatic approach to real-time 3D face reconstruction
from monocular in-the-wild videos. With the use of a cascaded-regressor based
face tracking and a 3D Morphable Face Model shape fitting, we obtain a
semi-dense 3D face shape. We further use the texture information from multiple
frames to build a holistic 3D face representation from the video frames. Our
system is able to capture facial expressions and does not require any
person-specific training. We demonstrate the robustness of our approach on the
challenging 300 Videos in the Wild (300-VW) dataset. Our real-time fitting
framework is available as an open source library at http://4dface.org
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
Fitting 3D Morphable Models using Local Features
In this paper, we propose a novel fitting method that uses local image
features to fit a 3D Morphable Model to 2D images. To overcome the obstacle of
optimising a cost function that contains a non-differentiable feature
extraction operator, we use a learning-based cascaded regression method that
learns the gradient direction from data. The method allows to simultaneously
solve for shape and pose parameters. Our method is thoroughly evaluated on
Morphable Model generated data and first results on real data are presented.
Compared to traditional fitting methods, which use simple raw features like
pixel colour or edge maps, local features have been shown to be much more
robust against variations in imaging conditions. Our approach is unique in that
we are the first to use local features to fit a Morphable Model.
Because of the speed of our method, it is applicable for realtime
applications. Our cascaded regression framework is available as an open source
library (https://github.com/patrikhuber).Comment: Submitted to ICIP 2015; 4 pages, 4 figure
Using 3D morphable models for face recognition in video
The 3D Morphable Face Model (3DMM) has been used for over a decade for creating 3D models from single images of faces. This model is based on a PCA model of the 3D shape and texture generated from a limited number of 3D scans. The goal of fitting a 3DMM to an image is to find the model coefficients, the lighting and other imaging variables from which we can remodel that image as accurately as possible. The model coefficients consist of texture and of shape descriptors, and can without further processing be used in verification and recognition experiments. Until now little research has been performed into the influence of the diverse parameters of the 3DMM on the recognition performance. In this paper we will introduce a Bayesian-based method for texture backmapping from multiple images. Using the information from multiple (non-frontal) views we construct a frontal view which can be used as input to 2D face recognition software. We also show how the number of triangles used in the fitting proces influences the recognition performance using the shape descriptors. The verification results of the 3DMM are compared to state-of-the-art 2D face recognition software on the MultiPIE dataset. The 2D FR software outperforms the Morphable Model, but the Morphable Model can be useful as a preprocesser to synthesize a frontal view from a non-frontal view and also combine images with multiple views to a single frontal view. We show results for this preprocessing technique by using an average face shape, a fitted face shape, with a MM texture, with the original texture and with a hybrid texture. The preprocessor has improved the verification results significantly on the dataset
Fitting a 3D Morphable Model to Edges: A Comparison Between Hard and Soft Correspondences
We propose a fully automatic method for fitting a 3D morphable model to
single face images in arbitrary pose and lighting. Our approach relies on
geometric features (edges and landmarks) and, inspired by the iterated closest
point algorithm, is based on computing hard correspondences between model
vertices and edge pixels. We demonstrate that this is superior to previous work
that uses soft correspondences to form an edge-derived cost surface that is
minimised by nonlinear optimisation.Comment: To appear in ACCV 2016 Workshop on Facial Informatic
Unsupervised Training for 3D Morphable Model Regression
We present a method for training a regression network from image pixels to 3D
morphable model coordinates using only unlabeled photographs. The training loss
is based on features from a facial recognition network, computed on-the-fly by
rendering the predicted faces with a differentiable renderer. To make training
from features feasible and avoid network fooling effects, we introduce three
objectives: a batch distribution loss that encourages the output distribution
to match the distribution of the morphable model, a loopback loss that ensures
the network can correctly reinterpret its own output, and a multi-view identity
loss that compares the features of the predicted 3D face and the input
photograph from multiple viewing angles. We train a regression network using
these objectives, a set of unlabeled photographs, and the morphable model
itself, and demonstrate state-of-the-art results.Comment: CVPR 2018 version with supplemental material
(http://openaccess.thecvf.com/content_cvpr_2018/html/Genova_Unsupervised_Training_for_CVPR_2018_paper.html
Video Interpolation using Optical Flow and Laplacian Smoothness
Non-rigid video interpolation is a common computer vision task. In this paper
we present an optical flow approach which adopts a Laplacian Cotangent Mesh
constraint to enhance the local smoothness. Similar to Li et al., our approach
adopts a mesh to the image with a resolution up to one vertex per pixel and
uses angle constraints to ensure sensible local deformations between image
pairs. The Laplacian Mesh constraints are expressed wholly inside the optical
flow optimization, and can be applied in a straightforward manner to a wide
range of image tracking and registration problems. We evaluate our approach by
testing on several benchmark datasets, including the Middlebury and Garg et al.
datasets. In addition, we show application of our method for constructing 3D
Morphable Facial Models from dynamic 3D data
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
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