45,627 research outputs found
3D face reconstruction with geometry details from a single image
3D face reconstruction from a single image is a classical and challenging problem with wide applications in many areas. Inspired by recent works in face animation from RGB-D or monocular video inputs, we develop a novel method for reconstructing 3D faces from unconstrained 2D images using a coarse-to-fine optimization strategy. First, a smooth coarse 3D face is generated from an example-based bilinear face model by aligning the projection of 3D face landmarks with 2D landmarks detected from the input image. Afterward, using local corrective deformation fields, the coarse 3D face is refined using photometric consistency constraints, resulting in a medium face shape. Finally, a shape-from-shading method is applied on the medium face to recover fine geometric details. Our method outperforms the state-of-the-art approaches in terms of accuracy and detail recovery, which is demonstrated in extensive experiments using real-world models and publicly available data sets
FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face Reconstruction
In this paper, we present a large-scale detailed 3D face dataset, FaceScape,
and the corresponding benchmark to evaluate single-view facial 3D
reconstruction. By training on FaceScape data, a novel algorithm is proposed to
predict elaborate riggable 3D face models from a single image input. FaceScape
dataset provides 18,760 textured 3D faces, captured from 938 subjects and each
with 20 specific expressions. The 3D models contain the pore-level facial
geometry that is also processed to be topologically uniformed. These fine 3D
facial models can be represented as a 3D morphable model for rough shapes and
displacement maps for detailed geometry. Taking advantage of the large-scale
and high-accuracy dataset, a novel algorithm is further proposed to learn the
expression-specific dynamic details using a deep neural network. The learned
relationship serves as the foundation of our 3D face prediction system from a
single image input. Different than the previous methods, our predicted 3D
models are riggable with highly detailed geometry under different expressions.
We also use FaceScape data to generate the in-the-wild and in-the-lab benchmark
to evaluate recent methods of single-view face reconstruction. The accuracy is
reported and analyzed on the dimensions of camera pose and focal length, which
provides a faithful and comprehensive evaluation and reveals new challenges.
The unprecedented dataset, benchmark, and code have been released to the public
for research purpose.Comment: 14 pages, 13 figures, journal extension of FaceScape(CVPR 2020).
arXiv admin note: substantial text overlap with arXiv:2003.1398
Lightweight Photometric Stereo for Facial Details Recovery
Recently, 3D face reconstruction from a single image has achieved great
success with the help of deep learning and shape prior knowledge, but they
often fail to produce accurate geometry details. On the other hand, photometric
stereo methods can recover reliable geometry details, but require dense inputs
and need to solve a complex optimization problem. In this paper, we present a
lightweight strategy that only requires sparse inputs or even a single image to
recover high-fidelity face shapes with images captured under near-field lights.
To this end, we construct a dataset containing 84 different subjects with 29
expressions under 3 different lights. Data augmentation is applied to enrich
the data in terms of diversity in identity, lighting, expression, etc. With
this constructed dataset, we propose a novel neural network specially designed
for photometric stereo based 3D face reconstruction. Extensive experiments and
comparisons demonstrate that our method can generate high-quality
reconstruction results with one to three facial images captured under
near-field lights. Our full framework is available at
https://github.com/Juyong/FacePSNet.Comment: Accepted to CVPR2020. The source code is available
https://github.com/Juyong/FacePSNe
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 And Emotion Analytics With Part-Based Morphable Models
3D face reconstruction and facial expression analytics using 3D facial data are new
and hot research topics in computer graphics and computer vision. In this proposal, we first
review the background knowledge for emotion analytics using 3D morphable face model, including
geometry feature-based methods, statistic model-based methods and more advanced
deep learning-bade methods. Then, we introduce a novel 3D face modeling and reconstruction
solution that robustly and accurately acquires 3D face models from a couple of images
captured by a single smartphone camera. Two selfie photos of a subject taken from the
front and side are used to guide our Non-Negative Matrix Factorization (NMF) induced
part-based face model to iteratively reconstruct an initial 3D face of the subject. Then, an
iterative detail updating method is applied to the initial generated 3D face to reconstruct
facial details through optimizing lighting parameters and local depths. Our iterative 3D
face reconstruction method permits fully automatic registration of a part-based face representation
to the acquired face data and the detailed 2D/3D features to build a high-quality
3D face model. The NMF part-based face representation learned from a 3D face database
facilitates effective global and adaptive local detail data fitting alternatively. Our system
is flexible and it allows users to conduct the capture in any uncontrolled environment. We
demonstrate the capability of our method by allowing users to capture and reconstruct their
3D faces by themselves.
Based on the 3D face model reconstruction, we can analyze the facial expression and
the related emotion in 3D space. We present a novel approach to analyze the facial expressions
from images and a quantitative information visualization scheme for exploring this
type of visual data. From the reconstructed result using NMF part-based morphable 3D face
model, basis parameters and a displacement map are extracted as features for facial emotion
analysis and visualization. Based upon the features, two Support Vector Regressions (SVRs)
are trained to determine the fuzzy Valence-Arousal (VA) values to quantify the emotions.
The continuously changing emotion status can be intuitively analyzed by visualizing the
VA values in VA-space. Our emotion analysis and visualization system, based on 3D NMF
morphable face model, detects expressions robustly from various head poses, face sizes and
lighting conditions, and is fully automatic to compute the VA values from images or a sequence
of video with various facial expressions. To evaluate our novel method, we test our
system on publicly available databases and evaluate the emotion analysis and visualization
results. We also apply our method to quantifying emotion changes during motivational interviews.
These experiments and applications demonstrate effectiveness and accuracy of
our method.
In order to improve the expression recognition accuracy, we present a facial expression
recognition approach with 3D Mesh Convolutional Neural Network (3DMCNN) and a visual
analytics guided 3DMCNN design and optimization scheme. The geometric properties of the
surface is computed using the 3D face model of a subject with facial expressions. Instead of
using regular Convolutional Neural Network (CNN) to learn intensities of the facial images,
we convolve the geometric properties on the surface of the 3D model using 3DMCNN. We
design a geodesic distance-based convolution method to overcome the difficulties raised from
the irregular sampling of the face surface mesh. We further present an interactive visual
analytics for the purpose of designing and modifying the networks to analyze the learned
features and cluster similar nodes in 3DMCNN. By removing low activity nodes in the network,
the performance of the network is greatly improved. We compare our method with the regular CNN-based method by interactively visualizing each layer of the networks and
analyze the effectiveness of our method by studying representative cases. Testing on public
datasets, our method achieves a higher recognition accuracy than traditional image-based
CNN and other 3D CNNs. The presented framework, including 3DMCNN and interactive
visual analytics of the CNN, can be extended to other applications
3D Face Reconstruction by Learning from Synthetic Data
Fast and robust three-dimensional reconstruction of facial geometric
structure from a single image is a challenging task with numerous applications.
Here, we introduce a learning-based approach for reconstructing a
three-dimensional face from a single image. Recent face recovery methods rely
on accurate localization of key characteristic points. In contrast, the
proposed approach is based on a Convolutional-Neural-Network (CNN) which
extracts the face geometry directly from its image. Although such deep
architectures outperform other models in complex computer vision problems,
training them properly requires a large dataset of annotated examples. In the
case of three-dimensional faces, currently, there are no large volume data
sets, while acquiring such big-data is a tedious task. As an alternative, we
propose to generate random, yet nearly photo-realistic, facial images for which
the geometric form is known. The suggested model successfully recovers facial
shapes from real images, even for faces with extreme expressions and under
various lighting conditions.Comment: The first two authors contributed equally to this wor
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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