1,613 research outputs found
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
From 3D Point Clouds to Pose-Normalised Depth Maps
We consider the problem of generating either pairwise-aligned or pose-normalised depth maps from noisy 3D point clouds in a relatively unrestricted poses. Our system is deployed in a 3D face alignment application and consists of the following four stages: (i) data filtering, (ii) nose tip identification and sub-vertex localisation, (iii) computation of the (relative) face orientation, (iv) generation of either a pose aligned or a pose normalised depth map. We generate an implicit radial basis function (RBF) model of the facial surface and this is employed within all four stages of the process. For example, in stage (ii), construction of novel invariant features is based on sampling this RBF over a set of concentric spheres to give a spherically-sampled RBF (SSR) shape histogram. In stage (iii), a second novel descriptor, called an isoradius contour curvature signal, is defined, which allows rotational alignment to be determined using a simple process of 1D correlation. We test our system on both the University of York (UoY) 3D face dataset and the Face Recognition Grand Challenge (FRGC) 3D data. For the more challenging UoY data, our SSR descriptors significantly outperform three variants of spin images, successfully identifying nose vertices at a rate of 99.6%. Nose localisation performance on the higher quality FRGC data, which has only small pose variations, is 99.9%. Our best system successfully normalises the pose of 3D faces at rates of 99.1% (UoY data) and 99.6% (FRGC data)
Unsupervised Classification of Intrusive Igneous Rock Thin Section Images using Edge Detection and Colour Analysis
Classification of rocks is one of the fundamental tasks in a geological
study. The process requires a human expert to examine sampled thin section
images under a microscope. In this study, we propose a method that uses
microscope automation, digital image acquisition, edge detection and colour
analysis (histogram). We collected 60 digital images from 20 standard thin
sections using a digital camera mounted on a conventional microscope. Each
image is partitioned into a finite number of cells that form a grid structure.
Edge and colour profile of pixels inside each cell determine its
classification. The individual cells then determine the thin section image
classification via a majority voting scheme. Our method yielded successful
results as high as 90% to 100% precision.Comment: To appear in 2017 IEEE International Conference On Signal and Image
Processing Application
Automatic construction of robust spherical harmonic subspaces
In this paper we propose a method to automatically recover a class specific low dimensional spherical harmonic basis from a set of in-the-wild facial images. We combine existing techniques for uncalibrated photometric stereo and low rank matrix decompositions in order to robustly recover a combined model of shape and identity. We build this basis without aid from a 3D model and show how it can be combined with recent efficient sparse facial feature localisation techniques to recover dense 3D facial shape. Unlike previous works in the area, our method is very efficient and is an order of magnitude faster to train, taking only a few minutes to build a model with over 2000 images. Furthermore, it can be used for real-time recovery of facial shape
Synthesizing Coupled 3D Face Modalities by Trunk-Branch Generative Adversarial Networks
Generating realistic 3D faces is of high importance for computer graphics and
computer vision applications. Generally, research on 3D face generation
revolves around linear statistical models of the facial surface. Nevertheless,
these models cannot represent faithfully either the facial texture or the
normals of the face, which are very crucial for photo-realistic face synthesis.
Recently, it was demonstrated that Generative Adversarial Networks (GANs) can
be used for generating high-quality textures of faces. Nevertheless, the
generation process either omits the geometry and normals, or independent
processes are used to produce 3D shape information. In this paper, we present
the first methodology that generates high-quality texture, shape, and normals
jointly, which can be used for photo-realistic synthesis. To do so, we propose
a novel GAN that can generate data from different modalities while exploiting
their correlations. Furthermore, we demonstrate how we can condition the
generation on the expression and create faces with various facial expressions.
The qualitative results shown in this paper are compressed due to size
limitations, full-resolution results and the accompanying video can be found in
the supplementary documents. The code and models are available at the project
page: https://github.com/barisgecer/TBGAN.Comment: Check project page: https://github.com/barisgecer/TBGAN for the full
resolution results and the accompanying vide
Automatic construction of robust spherical harmonic subspaces
In this paper we propose a method to automatically recover a class specific low dimensional spherical harmonic basis from a set of in-the-wild facial images. We combine existing techniques for uncalibrated photometric stereo and low rank matrix decompositions in order to robustly recover a combined model of shape and identity. We build this basis without aid from a 3D model and show how it can be combined with recent efficient sparse facial feature localisation techniques to recover dense 3D facial shape. Unlike previous works in the area, our method is very efficient and is an order of magnitude faster to train, taking only a few minutes to build a model with over 2000 images. Furthermore, it can be used for real-time recovery of facial shape
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