303 research outputs found
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
Generating 3D faces using Convolutional Mesh Autoencoders
Learned 3D representations of human faces are useful for computer vision
problems such as 3D face tracking and reconstruction from images, as well as
graphics applications such as character generation and animation. Traditional
models learn a latent representation of a face using linear subspaces or
higher-order tensor generalizations. Due to this linearity, they can not
capture extreme deformations and non-linear expressions. To address this, we
introduce a versatile model that learns a non-linear representation of a face
using spectral convolutions on a mesh surface. We introduce mesh sampling
operations that enable a hierarchical mesh representation that captures
non-linear variations in shape and expression at multiple scales within the
model. In a variational setting, our model samples diverse realistic 3D faces
from a multivariate Gaussian distribution. Our training data consists of 20,466
meshes of extreme expressions captured over 12 different subjects. Despite
limited training data, our trained model outperforms state-of-the-art face
models with 50% lower reconstruction error, while using 75% fewer parameters.
We also show that, replacing the expression space of an existing
state-of-the-art face model with our autoencoder, achieves a lower
reconstruction error. Our data, model and code are available at
http://github.com/anuragranj/com
A Data-augmented 3D Morphable Model of the Ear
Morphable models are useful shape priors for biometric recognition tasks. Here we present an iterative process of refinement for a 3D Morphable Model (3DMM) of the human ear that employs data augmentation. The process employs the following stages 1) landmark-based 3DMM fitting; 2) 3D template deformation to overcome noisy over-fitting; 3) 3D mesh editing, to improve the fit to manual 2D landmarks. These processes are wrapped in an iterative procedure that is able to bootstrap a weak, approximate model into a significantly better model. Evaluations using several performance metrics verify the improvement of our model using the proposed algorithm. We use this new 3DMM model-booting algorithm to generate a refined 3D morphable model of the human ear, and we make this new model and our augmented training dataset public
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