LaughTalk: Expressive 3D Talking Head Generation with Laughter

Laughter is a unique expression, essential to affirmative social interactions of humans. Although current 3D talking head generation methods produce convincing verbal articulations, they often fail to capture the vitality and subtleties of laughter and smiles despite their importance in social context. In this paper, we introduce a novel task to generate 3D talking heads capable of both articulate speech and authentic laughter. Our newly curated dataset comprises 2D laughing videos paired with pseudo-annotated and human-validated 3D FLAME parameters and vertices. Given our proposed dataset, we present a strong baseline with a two-stage training scheme: the model first learns to talk and then acquires the ability to express laughter. Extensive experiments demonstrate that our method performs favorably compared to existing approaches in both talking head generation and expressing laughter signals. We further explore potential applications on top of our proposed method for rigging realistic avatars.Comment: Accepted to WACV202

Visual units and confusion modelling for automatic lip-reading

Automatic lip-reading (ALR) is a challenging task because the visual speech signal is known to be missing some important information, such as voicing. We propose an approach to ALR that acknowledges that this information is missing but assumes that it is substituted or deleted in a systematic way that can be modelled. We describe a system that learns such a model and then incorporates it into decoding, which is realised as a cascade of weighted finite-state transducers. Our results show a small but statistically significant improvement in recognition accuracy. We also investigate the issue of suitable visual units for ALR, and show that visemes are sub-optimal, not but because they introduce lexical ambiguity, but because the reduction in modelling units entailed by their use reduces accuracy

Final Report to NSF of the Standards for Facial Animation Workshop

The human face is an important and complex communication channel. It is a very familiar and sensitive object of human perception. The facial animation field has increased greatly in the past few years as fast computer graphics workstations have made the modeling and real-time animation of hundreds of thousands of polygons affordable and almost commonplace. Many applications have been developed such as teleconferencing, surgery, information assistance systems, games, and entertainment. To solve these different problems, different approaches for both animation control and modeling have been developed

Registration of 3D Point Clouds and Meshes: A Survey From Rigid to Non-Rigid

Three-dimensional surface registration transforms multiple three-dimensional data sets into the same coordinate system so as to align overlapping components of these sets. Recent surveys have covered different aspects of either rigid or nonrigid registration, but seldom discuss them as a whole. Our study serves two purposes: 1) To give a comprehensive survey of both types of registration, focusing on three-dimensional point clouds and meshes and 2) to provide a better understanding of registration from the perspective of data fitting. Registration is closely related to data fitting in which it comprises three core interwoven components: model selection, correspondences and constraints, and optimization. Study of these components 1) provides a basis for comparison of the novelties of different techniques, 2) reveals the similarity of rigid and nonrigid registration in terms of problem representations, and 3) shows how overfitting arises in nonrigid registration and the reasons for increasing interest in intrinsic techniques. We further summarize some practical issues of registration which include initializations and evaluations, and discuss some of our own observations, insights and foreseeable research trends

Emerging Linguistic Functions in Early Infancy

This paper presents results from experimental studies on early language acquisition in infants and attempts to interpret the experimental results within the framework of the Ecological Theory of Language Acquisition (ETLA) recently proposed by (Lacerda et al., 2004a). From this perspective, the infant’s first steps in the acquisition of the ambient language are seen as a consequence of the infant’s general capacity to represent sensory input and the infant’s interaction with other actors in its immediate ecological environment. On the basis of available experimental evidence, it will be argued that ETLA offers a productive alternative to traditional descriptive views of the language acquisition process by presenting an operative model of how early linguistic function may emerge through interaction

New method for mathematical modelling of human visual speech

Audio-visual speech recognition and visual speech synthesisers are used as interfaces between humans and machines. Such interactions specifically rely on the analysis and synthesis of both audio and visual information, which humans use for face-to-face communication. Currently, there is no global standard to describe these interactions nor is there a standard mathematical tool to describe lip movements. Furthermore, the visual lip movement for each phoneme is considered in isolation rather than a continuation from one to another. Consequently, there is no globally accepted standard method for representing lip movement during articulation. This thesis addresses these issues by designing a transcribed group of words, by mathematical formulas, and so introducing the concept of a visual word, allocating signatures to visual words and finally building a visual speech vocabulary database. In addition, visual speech information has been analysed in a novel way by considering both lip movements and phonemic structure of the English language. In order to extract the visual data, three visual features on the lip have been chosen; these are on the outer upper, lower and corner of the lip. The extracted visual data during articulation is called the visual speech sample set. The final visual data is obtained after processing the visual speech sample sets to correct experimented artefacts such as head tilting, which happened during articulation and visual data extraction. The ‘Barycentric Lagrange Interpolation’ (BLI) formulates the visual speech sample sets into visual speech signals. The visual word is defined in this work and consists of the variation of three visual features. Further processing on relating the visual speech signals to the uttered word leads to the allocation of signatures that represent the visual word. This work suggests the visual word signature can be used either as a ‘visual word barcode’, a ‘digital visual word’ or a ‘2D/3D representations’. The 2D version of the visual word provides a unique signature that allows the identification of the words being uttered. In addition, identification of visual words has also been performed using a technique called ‘volumetric representations of the visual words’. Furthermore, the effect of altering the amplitudes and sampling rate for BLI has been evaluated. In addition, the performance of BLI in reconstructing the visual speech sample sets has been considered. Finally, BLI has been compared to signal reconstruction approach by RMSE and correlation coefficients. The results show that the BLI is the more reliable method for the purpose of this work according to Section 7.7

Statistical/Geometric Techniques for Object Representation and Recognition

Object modeling and recognition are key areas of research in computer vision and graphics with wide range of applications. Though research in these areas is not new, traditionally most of it has focused on analyzing problems under controlled environments. The challenges posed by real life applications demand for more general and robust solutions. The wide variety of objects with large intra-class variability makes the task very challenging. The difficulty in modeling and matching objects also vary depending on the input modality. In addition, the easy availability of sensors and storage have resulted in tremendous increase in the amount of data that needs to be processed which requires efficient algorithms suitable for large-size databases. In this dissertation, we address some of the challenges involved in modeling and matching of objects in realistic scenarios. Object matching in images require accounting for large variability in the appearance due to changes in illumination and view point. Any real world object is characterized by its underlying shape and albedo, which unlike the image intensity are insensitive to changes in illumination conditions. We propose a stochastic filtering framework for estimating object albedo from a single intensity image by formulating the albedo estimation as an image estimation problem. We also show how this albedo estimate can be used for illumination insensitive object matching and for more accurate shape recovery from a single image using standard shape from shading formulation. We start with the simpler problem where the pose of the object is known and only the illumination varies. We then extend the proposed approach to handle unknown pose in addition to illumination variations. We also use the estimated albedo maps for another important application, which is recognizing faces across age progression. Many approaches which address the problem of modeling and recognizing objects from images assume that the underlying objects are of diffused texture. But most real world objects exhibit a combination of diffused and specular properties. We propose an approach for separating the diffused and specular reflectance from a given color image so that the algorithms proposed for objects of diffused texture become applicable to a much wider range of real world objects. Representing and matching the 2D and 3D geometry of objects is also an integral part of object matching with applications in gesture recognition, activity classification, trademark and logo recognition, etc. The challenge in matching 2D/3D shapes lies in accounting for the different rigid and non-rigid deformations, large intra-class variability, noise and outliers. In addition, since shapes are usually represented as a collection of landmark points, the shape matching algorithm also has to deal with the challenges of missing or unknown correspondence across these data points. We propose an efficient shape indexing approach where the different feature vectors representing the shape are mapped to a hash table. For a query shape, we show how the similar shapes in the database can be efficiently retrieved without the need for establishing correspondence making the algorithm extremely fast and scalable. We also propose an approach for matching and registration of 3D point cloud data across unknown or missing correspondence using an implicit surface representation. Finally, we discuss possible future directions of this research

Interactive speech-driven facial animation

One of the fastest developing areas in the entertainment industry is digital animation. Television programmes and movies frequently use 3D animations to enhance or replace actors and scenery. With the increase in computing power, research is also being done to apply these animations in an interactive manner. Two of the biggest obstacles to the success of these undertakings are control (manipulating the models) and realism. This text describes many of the ways to improve control and realism aspects, in such a way that interactive animation becomes possible. Specifically, lip-synchronisation (driven by human speech), and various modeling and rendering techniques are discussed. A prototype that shows that interactive animation is feasible, is also described.Mr. A. Hardy Prof. S. von Solm

Subspace Representations for Robust Face and Facial Expression Recognition

Analyzing human faces and modeling their variations have always been of interest to the computer vision community. Face analysis based on 2D intensity images is a challenging problem, complicated by variations in pose, lighting, blur, and non-rigid facial deformations due to facial expressions. Among the different sources of variation, facial expressions are of interest as important channels of non-verbal communication. Facial expression analysis is also affected by changes in view-point and inter-subject variations in performing different expressions. This dissertation makes an attempt to address some of the challenges involved in developing robust algorithms for face and facial expression recognition by exploiting the idea of proper subspace representations for data. Variations in the visual appearance of an object mostly arise due to changes in illumination and pose. So we first present a video-based sequential algorithm for estimating the face albedo as an illumination-insensitive signature for face recognition. We show that by knowing/estimating the pose of the face at each frame of a sequence, the albedo can be efficiently estimated using a Kalman filter. Then we extend this to the case of unknown pose by simultaneously tracking the pose as well as updating the albedo through an efficient Bayesian inference method performed using a Rao-Blackwellized particle filter. Since understanding the effects of blur, especially motion blur, is an important problem in unconstrained visual analysis, we then propose a blur-robust recognition algorithm for faces with spatially varying blur. We model a blurred face as a weighted average of geometrically transformed instances of its clean face. We then build a matrix, for each gallery face, whose column space spans the space of all the motion blurred images obtained from the clean face. This matrix representation is then used to define a proper objective function and perform blur-robust face recognition. To develop robust and generalizable models for expression analysis one needs to break the dependence of the models on the choice of the coordinate frame of the camera. To this end, we build models for expressions on the affine shape-space (Grassmann manifold), as an approximation to the projective shape-space, by using a Riemannian interpretation of deformations that facial expressions cause on different parts of the face. This representation enables us to perform various expression analysis and recognition algorithms without the need for pose normalization as a preprocessing step. There is a large degree of inter-subject variations in performing various expressions. This poses an important challenge on developing robust facial expression recognition algorithms. To address this challenge, we propose a dictionary-based approach for facial expression analysis by decomposing expressions in terms of action units (AUs). First, we construct an AU-dictionary using domain experts' knowledge of AUs. To incorporate the high-level knowledge regarding expression decomposition and AUs, we then perform structure-preserving sparse coding by imposing two layers of grouping over AU-dictionary atoms as well as over the test image matrix columns. We use the computed sparse code matrix for each expressive face to perform expression decomposition and recognition. Most of the existing methods for the recognition of faces and expressions consider either the expression-invariant face recognition problem or the identity-independent facial expression recognition problem. We propose joint face and facial expression recognition using a dictionary-based component separation algorithm (DCS). In this approach, the given expressive face is viewed as a superposition of a neutral face component with a facial expression component, which is sparse with respect to the whole image. This assumption leads to a dictionary-based component separation algorithm, which benefits from the idea of sparsity and morphological diversity. The DCS algorithm uses the data-driven dictionaries to decompose an expressive test face into its constituent components. The sparse codes we obtain as a result of this decomposition are then used for joint face and expression recognition

Registration and statistical analysis of the tongue shape during speech production

This thesis analyzes the human tongue shape during speech production. First, a semi-supervised approach is derived for estimating the tongue shape from volumetric magnetic resonance imaging data of the human vocal tract. Results of this extraction are used to derive parametric tongue models. Next, a framework is presented for registering sparse motion capture data of the tongue by means of such a model. This method allows to generate full three-dimensional animations of the tongue. Finally, a multimodal and statistical text-to-speech system is developed that is able to synthesize audio and synchronized tongue motion from text.Diese Dissertation beschäftigt sich mit der Analyse der menschlichen Zungenform während der Sprachproduktion. Zunächst wird ein semi-überwachtes Verfahren vorgestellt, mit dessen Hilfe sich Zungenformen von volumetrischen Magnetresonanztomographie- Aufnahmen des menschlichen Vokaltrakts schätzen lassen. Die Ergebnisse dieses Extraktionsverfahrens werden genutzt, um ein parametrisches Zungenmodell zu konstruieren. Danach wird eine Methode hergeleitet, die ein solches Modell nutzt, um spärliche Bewegungsaufnahmen der Zunge zu registrieren. Dieser Ansatz erlaubt es, dreidimensionale Animationen der Zunge zu erstellen. Zuletzt wird ein multimodales und statistisches Text-to-Speech-System entwickelt, das in der Lage ist, Audio und die dazu synchrone Zungenbewegung zu synthetisieren.German Research Foundatio