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

Face Transfer with Multilinear Models

Face Transfer is a method for mapping videorecorded performances of one individual to facial animations of another. It extracts visemes (speech-related mouth articulations), expressions, and three-dimensional (3D) pose from monocular video or film footage. These parameters are then used to generate and drive a detailed 3D textured face mesh for a target identity, which can be seamlessly rendered back into target footage. The underlying face model automatically adjusts for how the target performs facial expressions and visemes. The performance data can be easily edited to change the visemes, expressions, pose, or even the identity of the target---the attributes are separably controllable. This supports a wide variety of video rewrite and puppetry applications.Face Transfer is based on a multilinear model of 3D face meshes that separably parameterizes the space of geometric variations due to different attributes (e.g., identity, expression, and viseme). Separability means that each of these attributes can be independently varied. A multilinear model can be estimated from a Cartesian product of examples (identities x expressions x visemes) with techniques from statistical analysis, but only after careful preprocessing of the geometric data set to secure one-to-one correspondence, to minimize cross-coupling artifacts, and to fill in any missing examples. Face Transfer offers new solutions to these problems and links the estimated model with a face-tracking algorithm to extract pose, expression, and viseme parameters.Engineering and Applied Science

Estimation of 3D Faces and Illumination from Single Photographs Using a Bilineaur Illumination Model

3D Face modeling is still one of the biggest challenges in computer graphics. In this paper we present a novel framework that acquires the 3D shape, texture, pose and illumination of a face from a single photograph. Additionally, we show how we can recreate a face under varying illumination conditions. Or, essentially relight it. Using a custom-built face scanning system, we have collected 3D face scans and light reflection images of a large and diverse group of human subjects . We derive a morphable face model for 3D face shapes and accompanying textures by transforming the data into a linear vector sub-space. The acquired images of faces under variable illumination are then used to derive a bilinear illumination model that spans 3D face shape and illumination variations. Using both models we, in turn, propose a novel fitting framework that estimates the parameters of the morphable model given a single photograph. Our framework can deal with complex face reflectance and lighting environments in an efficient and robust manner. In the results section of our paper, we compare our methods to existing ones and demonstrate its efficacy in reconstructing 3D face models when provided with a single photograph. We also provide several examples of facial relighting (on 2D images) by performing adequate decomposition of the estimated illumination using our framework.Engineering and Applied Science

A 3D morphable model learnt from 10,000 faces

This is the final version of the article. It is the open access version, provided by the Computer Vision Foundation. Except for the watermark, it is identical to the IEEE published version. Available from IEEE via the DOI in this record.We present Large Scale Facial Model (LSFM) - a 3D Morphable Model (3DMM) automatically constructed from 9,663 distinct facial identities. To the best of our knowledge LSFM is the largest-scale Morphable Model ever constructed, containing statistical information from a huge variety of the human population. To build such a large model we introduce a novel fully automated and robust Morphable Model construction pipeline. The dataset that LSFM is trained on includes rich demographic information about each subject, allowing for the construction of not only a global 3DMM but also models tailored for specific age, gender or ethnicity groups. As an application example, we utilise the proposed model to perform age classification from 3D shape alone. Furthermore, we perform a systematic analysis of the constructed 3DMMs that showcases their quality and descriptive power. The presented extensive qualitative and quantitative evaluations reveal that the proposed 3DMM achieves state-of-the-art results, outperforming existing models by a large margin. Finally, for the benefit of the research community, we make publicly available the source code of the proposed automatic 3DMM construction pipeline. In addition, the constructed global 3DMM and a variety of bespoke models tailored by age, gender and ethnicity are available on application to researchers involved in medically oriented research.J. Booth is funded by an EPSRC DTA from Imperial College London, and holds a Qualcomm Innovation Fellowship. A. Roussos is funded by the Great Ormond Street Hospital Childrens Charity (Face Value: W1037). The work of S. Zafeiriou was partially funded by the EPSRC project EP/J017787/1 (4D-FAB)

Multilinear methods for disentangling variations with applications to facial analysis

Several factors contribute to the appearance of an object in a visual scene, including pose, illumination, and deformation, among others. Each factor accounts for a source of variability in the data. It is assumed that the multiplicative interactions of these factors emulate the entangled variability, giving rise to the rich structure of visual object appearance. Disentangling such unobserved factors from visual data is a challenging task, especially when the data have been captured in uncontrolled recording conditions (also referred to as “in-the-wild”) and label information is not available. The work presented in this thesis focuses on disentangling the variations contained in visual data, in particular applied to 2D and 3D faces. The motivation behind this work lies in recent developments in the field, such as (i) the creation of large, visual databases for face analysis, with (ii) the need of extracting information without the use of labels and (iii) the need to deploy systems under demanding, real-world conditions. In the first part of this thesis, we present a method to synthesise plausible 3D expressions that preserve the identity of a target subject. This method is supervised as the model uses labels, in this case 3D facial meshes of people performing a defined set of facial expressions, to learn. The ability to synthesise an entire facial rig from a single neutral expression has a large range of applications both in computer graphics and computer vision, ranging from the ecient and cost-e↵ective creation of CG characters to scalable data generation for machine learning purposes. Unlike previous methods based on multilinear models, the proposed approach is capable to extrapolate well outside the sample pool, which allows it to accurately reproduce the identity of the target subject and create artefact-free expression shapes while requiring only a small input dataset. We introduce global-local multilinear models that leverage the strengths of expression-specific and identity-specific local models combined with coarse motion estimations from a global model. The expression-specific and identity-specific local models are built from di↵erent slices of the patch-wise local multilinear model. Experimental results show that we achieve high-quality, identity-preserving facial expression synthesis results that outperform existing methods both quantitatively and qualitatively. In the second part of this thesis, we investigate how the modes of variations from visual data can be extracted. Our assumption is that visual data has an underlying structure consisting of factors of variation and their interactions. Finding this structure and the factors is important as it would not only help us to better understand visual data but once obtained we can edit the factors for use in various applications. Shape from Shading and expression transfer are just two of the potential applications. To extract the factors of variation, several supervised methods have been proposed but they require both labels regarding the modes of variations and the same number of samples under all modes of variations. Therefore, their applicability is limited to well-organised data, usually captured in well-controlled conditions. We propose a novel general multilinear matrix decomposition method that discovers the multilinear structure of possibly incomplete sets of visual data in unsupervised setting. We demonstrate the applicability of the proposed method in several computer vision tasks, including Shape from Shading (SfS) (in the wild and with occlusion removal), expression transfer, and estimation of surface normals from images captured in the wild. Finally, leveraging the unsupervised multilinear method proposed as well as recent advances in deep learning, we propose a weakly supervised deep learning method for disentangling multiple latent factors of variation in face images captured in-the-wild. To this end, we propose a deep latent variable model, where we model the multiplicative interactions of multiple latent factors of variation explicitly as a multilinear structure. We demonstrate that the proposed approach indeed learns disentangled representations of facial expressions and pose, which can be used in various applications, including face editing, as well as 3D face reconstruction and classification of facial expression, identity and pose.Open Acces

Geometric Expression Invariant 3D Face Recognition using Statistical Discriminant Models

Currently there is no complete face recognition system that is invariant to all facial expressions. Although humans find it easy to identify and recognise faces regardless of changes in illumination, pose and expression, producing a computer system with a similar capability has proved to be particularly di cult. Three dimensional face models are geometric in nature and therefore have the advantage of being invariant to head pose and lighting. However they are still susceptible to facial expressions. This can be seen in the decrease in the recognition results using principal component analysis when expressions are added to a data set. In order to achieve expression-invariant face recognition systems, we have employed a tensor algebra framework to represent 3D face data with facial expressions in a parsimonious space. Face variation factors are organised in particular subject and facial expression modes. We manipulate this using single value decomposition on sub-tensors representing one variation mode. This framework possesses the ability to deal with the shortcomings of PCA in less constrained environments and still preserves the integrity of the 3D data. The results show improved recognition rates for faces and facial expressions, even recognising high intensity expressions that are not in the training datasets. We have determined, experimentally, a set of anatomical landmarks that best describe facial expression e ectively. We found that the best placement of landmarks to distinguish di erent facial expressions are in areas around the prominent features, such as the cheeks and eyebrows. Recognition results using landmark-based face recognition could be improved with better placement. We looked into the possibility of achieving expression-invariant face recognition by reconstructing and manipulating realistic facial expressions. We proposed a tensor-based statistical discriminant analysis method to reconstruct facial expressions and in particular to neutralise facial expressions. The results of the synthesised facial expressions are visually more realistic than facial expressions generated using conventional active shape modelling (ASM). We then used reconstructed neutral faces in the sub-tensor framework for recognition purposes. The recognition results showed slight improvement. Besides biometric recognition, this novel tensor-based synthesis approach could be used in computer games and real-time animation applications

Bayesian Robust Tensor Factorization for Incomplete Multiway Data

We propose a generative model for robust tensor factorization in the presence of both missing data and outliers. The objective is to explicitly infer the underlying low-CP-rank tensor capturing the global information and a sparse tensor capturing the local information (also considered as outliers), thus providing the robust predictive distribution over missing entries. The low-CP-rank tensor is modeled by multilinear interactions between multiple latent factors on which the column sparsity is enforced by a hierarchical prior, while the sparse tensor is modeled by a hierarchical view of Student-$t$ distribution that associates an individual hyperparameter with each element independently. For model learning, we develop an efficient closed-form variational inference under a fully Bayesian treatment, which can effectively prevent the overfitting problem and scales linearly with data size. In contrast to existing related works, our method can perform model selection automatically and implicitly without need of tuning parameters. More specifically, it can discover the groundtruth of CP rank and automatically adapt the sparsity inducing priors to various types of outliers. In addition, the tradeoff between the low-rank approximation and the sparse representation can be optimized in the sense of maximum model evidence. The extensive experiments and comparisons with many state-of-the-art algorithms on both synthetic and real-world datasets demonstrate the superiorities of our method from several perspectives.Comment: in IEEE Transactions on Neural Networks and Learning Systems, 201