Synthesization and reconstruction of 3D faces by deep neural networks

The past few decades have witnessed substantial progress towards 3D facial modelling and reconstruction as it is high importance for many computer vision and graphics applications including Augmented/Virtual Reality (AR/VR), computer games, movie post-production, image/video editing, medical applications, etc. In the traditional approaches, facial texture and shape are represented as triangle mesh that can cover identity and expression variation with non-rigid deformation. A dataset of 3D face scans is then densely registered into a common topology in order to construct a linear statistical model. Such models are called 3D Morphable Models (3DMMs) and can be used for 3D face synthesization or reconstruction by a single or few 2D face images. The works presented in this thesis focus on the modernization of these traditional techniques in the light of recent advances of deep learning and thanks to the availability of large-scale datasets. Ever since the introduction of 3DMMs by over two decades, there has been a lot of progress on it and they are still considered as one of the best methodologies to model 3D faces. Nevertheless, there are still several aspects of it that need to be upgraded to the "deep era". Firstly, the conventional 3DMMs are built by linear statistical approaches such as Principal Component Analysis (PCA) which omits high-frequency information by its nature. While this does not curtail shape, which is often smooth in the original data, texture models are heavily afflicted by losing high-frequency details and photorealism. Secondly, the existing 3DMM fitting approaches rely on very primitive (i.e. RGB values, sparse landmarks) or hand-crafted features (i.e. HOG, SIFT) as supervision that are sensitive to "in-the-wild" images (i.e. lighting, pose, occlusion), or somewhat missing identity/expression resemblance with the target image. Finally, shape, texture, and expression modalities are separately modelled by ignoring the correlation among them, placing a fundamental limit to the synthesization of semantically meaningful 3D faces. Moreover, photorealistic 3D face synthesis has not been studied thoroughly in the literature. This thesis attempts to address the above-mentioned issues by harnessing the power of deep neural network and generative adversarial networks as explained below: Due to the linear texture models, many of the state-of-the-art methods are still not capable of reconstructing facial textures with high-frequency details. For this, we take a radically different approach and build a high-quality texture model by Generative Adversarial Networks (GANs) that preserves details. That is, we utilize GANs to train a very powerful generator of facial texture in the UV space. And then show that it is possible to employ this generator network as a statistical texture prior to 3DMM fitting. The resulting texture reconstructions are plausible and photorealistic as GANs are faithful to the real-data distribution in both low- and high- frequency domains. Then, we revisit the conventional 3DMM fitting approaches making use of non-linear optimization to find the optimal latent parameters that best reconstruct the test image but under a new perspective. We propose to optimize the parameters with the supervision of pretrained deep identity features through our end-to-end differentiable framework. In order to be robust towards initialization and expedite the fitting process, we also propose a novel self-supervised regression-based approach. We demonstrate excellent 3D face reconstructions that are photorealistic and identity preserving and achieve for the first time, to the best of our knowledge, facial texture reconstruction with high-frequency details. In order to extend the non-linear texture model for photo-realistic 3D face synthesis, we present a methodology that generates high-quality texture, shape, and normals jointly. 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. Additionally, we study another approach for photo-realistic face synthesis by 3D guidance. This study proposes to generate 3D faces by linear 3DMM and then augment their 2D rendering by an image-to-image translation network to the photorealistic face domain. Both works demonstrate excellent photorealistic face synthesis and show that the generated faces are improving face recognition benchmarks as synthetic training data. Finally, we study expression reconstruction for personalized 3D face models where we improve generalization and robustness of expression encoding. First, we propose a 3D augmentation approach on 2D head-mounted camera images to increase robustness to perspective changes. And, we also propose to train generic expression encoder network by populating the number of identities with a novel multi-id personalized model training architecture in a self-supervised manner. Both approaches show promising results in both qualitative and quantitative experiments.Open Acces

Evaluation of Joint Multi-Instance Multi-Label Learning For Breast Cancer Diagnosis

Multi-instance multi-label (MIML) learning is a challenging problem in many aspects. Such learning approaches might be useful for many medical diagnosis applications including breast cancer detection and classification. In this study subset of digiPATH dataset (whole slide digital breast cancer histopathology images) are used for training and evaluation of six state-of-the-art MIML methods. At the end, performance comparison of these approaches are given by means of effective evaluation metrics. It is shown that MIML-kNN achieve the best performance that is %65.3 average precision, where most of other methods attain acceptable results as well

GANFIT: Generative adversarial network fitting for high fidelity 3D face reconstruction

In the past few years, a lot of work has been done to- wards reconstructing the 3D facial structure from single images by capitalizing on the power of Deep Convolutional Neural Networks (DCNNs). In the most recent works, differentiable renderers were employed in order to learn the relationship between the facial identity features and the parameters of a 3D morphable model for shape and texture. The texture features either correspond to components of a linear texture space or are learned by auto-encoders directly from in-the-wild images. In all cases, the quality of the facial texture reconstruction of the state-of-the-art methods is still not capable of modeling textures in high fidelity. In this paper, we take a radically different approach and harness the power of Generative Adversarial Networks (GANs) and DCNNs in order to reconstruct the facial texture and shape from single images. That is, we utilize GANs to train a very powerful generator of facial texture in UV space. Then, we revisit the original 3D Morphable Models (3DMMs) fitting approaches making use of non-linear optimization to find the optimal latent parameters that best reconstruct the test image but under a new perspective. We optimize the parameters with the supervision of pretrained deep identity features through our end-to-end differentiable framework. We demonstrate excellent results in photorealistic and identity preserving 3D face reconstructions and achieve for the first time, to the best of our knowledge, facial texture reconstruction with high-frequency details

Towards a complete 3D morphable model of the human head

Three-dimensional Morphable Models (3DMMs) are powerful statistical tools for representing the 3D shapes and textures of an object class. Here we present the most complete 3DMM of the human head to date that includes face, cranium, ears, eyes, teeth and tongue. To achieve this, we propose two methods for combining existing 3DMMs of different overlapping head parts: i. use a regressor to complete missing parts of one model using the other, ii. use the Gaussian Process framework to blend covariance matrices from multiple models. Thus we build a new combined face-and-head shape model that blends the variability and facial detail of an existing face model (the LSFM) with the full head modelling capability of an existing head model (the LYHM). Then we construct and fuse a highly-detailed ear model to extend the variation of the ear shape. Eye and eye region models are incorporated into the head model, along with basic models of the teeth, tongue and inner mouth cavity. The new model achieves state-of-the-art performance. We use our model to reconstruct full head representations from single, unconstrained images allowing us to parameterize craniofacial shape and texture, along with the ear shape, eye gaze and eye color.Comment: 18 pages, 18 figures, submitted to Transactions on Pattern Analysis and Machine Intelligence (TPAMI) on the 9th of October as an extension paper of the original oral CVPR paper : arXiv:1903.0378

Robust Egocentric Photo-realistic Facial Expression Transfer for Virtual Reality

Social presence, the feeling of being there with a real person, will fuel the next generation of communication systems driven by digital humans in virtual reality (VR). The best 3D video-realistic VR avatars that minimize the uncanny effect rely on person-specific (PS) models. However, these PS models are time-consuming to build and are typically trained with limited data variability, which results in poor generalization and robustness. Major sources of variability that affects the accuracy of facial expression transfer algorithms include using different VR headsets (e.g., camera configuration, slop of the headset), facial appearance changes over time (e.g., beard, make-up), and environmental factors (e.g., lighting, backgrounds). This is a major drawback for the scalability of these models in VR. This paper makes progress in overcoming these limitations by proposing an end-to-end multi-identity architecture (MIA) trained with specialized augmentation strategies. MIA drives the shape component of the avatar from three cameras in the VR headset (two eyes, one mouth), in untrained subjects, using minimal personalized information (i.e., neutral 3D mesh shape). Similarly, if the PS texture decoder is available, MIA is able to drive the full avatar (shape+texture) robustly outperforming PS models in challenging scenarios. Our key contribution to improve robustness and generalization, is that our method implicitly decouples, in an unsupervised manner, the facial expression from nuisance factors (e.g., headset, environment, facial appearance). We demonstrate the superior performance and robustness of the proposed method versus state-of-the-art PS approaches in a variety of experiments

Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model

We propose a novel end-to-end semi-supervised adversarial framework to generate photorealistic face images of new identities with wide ranges of expressions, poses, and illuminations conditioned by a 3D morphable model. Previous adversarial style-transfer methods either supervise their networks with large volume of paired data or use unpaired data with a highly under-constrained two-way generative framework in an unsupervised fashion. We introduce pairwise adversarial supervision to constrain two-way domain adaptation by a small number of paired real and synthetic images for training along with the large volume of unpaired data. Extensive qualitative and quantitative experiments are performed to validate our idea. Generated face images of new identities contain pose, lighting and expression diversity and qualitative results show that they are highly constraint by the synthetic input image while adding photorealism and retaining identity information. We combine face images generated by the proposed method with the real data set to train face recognition algorithms. We evaluated the model on two challenging data sets: LFW and IJB-A. We observe that the generated images from our framework consistently improves over the performance of deep face recognition network trained with Oxford VGG Face dataset and achieves comparable results to the state-of-the-art

Color-blob-based COSFIRE filters for object recognition

Most object recognition methods rely on contour-defined features obtained by edge detection or region segmentation. They are not robust to diffuse region boundaries. Furthermore, such methods do not exploit region color information. We propose color-blob-based COSFIRE (Combination of Shifted Filter Responses) filters to be selective for combinations of diffuse circular regions (blobs) in specific mutual spatial arrangements. Such a filter combines the responses of a certain selection of Difference-of-GausSians filters, essentially blob detectors, of different scales, in certain channels of a color space, and at certain relative positions to each other. Its parameters are determined learned in an automatic configuration process that analyzes the properties of a given prototype object of interest. We use these filters to compute features that are effective for the recognition of the prototype objects. We form feature vectors that we use With an SVM classifier. We evaluate the proposed method on a traffic sign (GTSRB) and a butterfly data sets. For the GTSRB data set we achieve a recognition rate of 98.94%, which is slightly higher than human performance and for the butterfly data set we achieve 89.029 The proposed color-blob-based COSFIRE filters are very effective and outperform the contour-based COSFIRE filters. A COSFIRE filter is trainable, it can be configured with a single prototype pattern and it does not require domain knowledge. (C) 2016 Elsevier B.V. All rights reserved

Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks.

Generalizability of algorithms for binary cancer vs. no cancer classification is unknown for clinically more significant multi-class scenarios where intermediate categories have different risk factors and treatment strategies. We present a system that classifies whole slide images (WSI) of breast biopsies into five diagnostic categories. First, a saliency detector that uses a pipeline of four fully convolutional networks, trained with samples from records of pathologists screenings, performs multi-scale localization of diagnostically relevant regions of interest in WSI. Then, a convolutional network, trained from consensus-derived reference samples, classifies image patches as non-proliferative or proliferative changes, atypical ductal hyperplasia, ductal carcinoma in situ, and invasive carcinoma. Finally, the saliency and classification maps are fused for pixel-wise labeling and slide-level categorization. Experiments using 240 WSI showed that both saliency detector and classifier networks performed better than competing algorithms, and the five-class slide-level accuracy of 55% was not statistically different from the predictions of 45 pathologists. We also present example visualizations of the learned representations for breast cancer diagnosis

FitMe: Deep Photorealistic 3D Morphable Model Avatars

In this paper, we introduce FitMe, a facial reflectance model and a differentiable rendering optimization pipeline, that can be used to acquire high-fidelity renderable human avatars from single or multiple images. The model consists of a multi-modal style-based generator, that captures facial appearance in terms of diffuse and specular reflectance, and a PCA-based shape model. We employ a fast differentiable rendering process that can be used in an optimization pipeline, while also achieving photorealistic facial shading. Our optimization process accurately captures both the facial reflectance and shape in high-detail, by exploiting the expressivity of the style-based latent representation and of our shape model. FitMe achieves state-of-the-art reflectance acquisition and identity preservation on single "in-the-wild" facial images, while it produces impressive scan-like results, when given multiple unconstrained facial images pertaining to the same identity. In contrast with recent implicit avatar reconstructions, FitMe requires only one minute and produces relightable mesh and texture-based avatars, that can be used by end-user applications.Comment: Accepted at CVPR 2023, project page at https://lattas.github.io/fitme , 17 pages including supplementary materia